Soliciting data indicating at least one objective occurrence in response to acquisition of data indicating at least one subjective user state

ABSTRACT

A computationally implemented method includes, but is not limited to: acquiring subjective user state data including data indicating at least one subjective user state associated with a user; soliciting, in response to the acquisition of the subjective user state data, objective occurrence data including data indicating occurrence of at least one objective occurrence; acquiring the objective occurrence data; and correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is related to and claims the benefit of theearliest available effective filing date(s) from the following listedapplication(s) (the “Related Applications”) (e.g., claims earliestavailable priority dates for other than provisional patent applicationsor claims benefits under 35 USC § 119(e) for provisional patentapplications, for any and all parent, grandparent, great-grandparent,etc. applications of the Related Application(s)). All subject matter ofthe Related Applications and of any and all parent, grandparent,great-grandparent, etc. applications of the Related Applications isincorporated herein by reference to the extent such subject matter isnot inconsistent herewith.

RELATED APPLICATIONS

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/313,659, entitled CORRELATING SUBJECTIVE USERSTATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming ShawnP. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, EricC. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 21 Nov. 2008, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/315,083, entitled CORRELATING SUBJECTIVE USERSTATES WITH OBJECTIVE OCCURRENCES ASSOCIATED WITH A USER, naming ShawnP. Firminger, Jason Garms, Edward K. Y. Jung, Chris D. Karkanias, EricC. Leuthardt, Royce A. Levien, Robert W. Lord, Mark A. Malamud, John D.Rinaldo, Jr., Clarence T. Tegreene, Kristin M. Tolle, and Lowell L.Wood, Jr., as inventors, filed 26 Nov. 2008, which is currentlyco-pending, or is an application of which a currently co-pendingapplication is entitled to the benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/319,135, entitled CORRELATING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONEOBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 31 Dec. 2008, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

For purposes of the USPTO extra-statutory requirements, the presentapplication constitutes a continuation-in-part of U.S. patentapplication Ser. No. 12/319,134, entitled CORRELATING DATA INDICATING ATLEAST ONE SUBJECTIVE USER STATE WITH DATA INDICATING AT LEAST ONEOBJECTIVE OCCURRENCE ASSOCIATED WITH A USER, naming Shawn P. Firminger;Jason Garms; Edward K. Y. Jung; Chris D. Karkanias; Eric C. Leuthardt;Royce A. Levien; Robert W. Lord; Mark A. Malamud; John D. Rinaldo, Jr.;Clarence T. Tegreene; Kristin M. Tolle; Lowell L. Wood, Jr. asinventors, filed 31 Dec. 2008, which is currently co-pending, or is anapplication of which a currently co-pending application is entitled tothe benefit of the filing date.

The United States Patent Office (USPTO) has published a notice to theeffect that the USPTO's computer programs require that patent applicantsreference both a serial number and indicate whether an application is acontinuation or continuation-in-part. Stephen G. Kunin, Benefit ofPrior-Filed Application, USPTO Official Gazette Mar. 18, 2003, availableat http://www.uspto.gov/web/offices/com/sol/og/2003/week11/patbene.htm.The present Applicant Entity (hereinafter “Applicant”) has providedabove a specific reference to the application(s) from which priority isbeing claimed as recited by statute. Applicant understands that thestatute is unambiguous in its specific reference language and does notrequire either a serial number or any characterization, such as“continuation” or “continuation-in-part,” for claiming priority to U.S.patent applications. Notwithstanding the foregoing, Applicantunderstands that the USPTO's computer programs have certain data entryrequirements, and hence Applicant is designating the present applicationas a continuation-in-part of its parent applications as set forth above,but expressly points out that such designations are not to be construedin any way as any type of commentary and/or admission as to whether ornot the present application contains any new matter in addition to thematter of its parent application(s).

All subject matter of the Related Applications and of any and allparent, grandparent, great-grandparent, etc. applications of the RelatedApplications is incorporated herein by reference to the extent suchsubject matter is not inconsistent herewith.

SUMMARY

A computationally implemented method includes, but is not limited to:acquiring subjective user state data including data indicating at leastone subjective user state associated with a user; soliciting, inresponse to the acquisition of the subjective user state data, objectiveoccurrence data including data indicating occurrence of at least oneobjective occurrence; acquiring the objective occurrence data; andcorrelating the subjective user state data with the objective occurrencedata. In addition to the foregoing, other method aspects are describedin the claims, drawings, and text forming a part of the presentdisclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting theherein-referenced method aspects; the circuitry and/or programming canbe virtually any combination of hardware, software, and/or firmwareconfigured to effect the herein-referenced method aspects depending uponthe design choices of the system designer.

A computationally implemented system includes, but is not limited to:means for acquiring subjective user state data including data indicatingat least one subjective user state associated with a user; means forsoliciting, in response to the acquisition of the subjective user statedata, objective occurrence data including data indicating occurrence ofat least one objective occurrence; means for acquiring the objectiveoccurrence data; and means for correlating the subjective user statedata with the objective occurrence data. In addition to the foregoing,other system aspects are described in the claims, drawings, and textforming a part of the present disclosure.

A computationally implemented system includes, but is not limited to:circuitry for acquiring subjective user state data including dataindicating at least one subjective user state associated with a user;circuitry for soliciting, in response to the acquisition of thesubjective user state data, objective occurrence data including dataindicating occurrence of at least one objective occurrence; circuitryfor acquiring the objective occurrence data; and circuitry forcorrelating the subjective user state data with the objective occurrencedata. In addition to the foregoing, other system aspects are describedin the claims, drawings, and text forming a part of the presentdisclosure.

A computer program product including a signal-bearing medium bearing oneor more instructions for acquiring subjective user state data includingdata indicating at least one subjective user state associated with auser; one or more instructions for soliciting, in response to theacquisition of the subjective user state data, objective occurrence dataincluding data indicating occurrence of at least one objectiveoccurrence; one or more instructions for acquiring the objectiveoccurrence data; and one or more instructions for correlating thesubjective user state data with the objective occurrence data. Inaddition to the foregoing, other computer program product aspects aredescribed in the claims, drawings, and text forming a part of thepresent disclosure.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1 a and 1 b show a high-level block diagram of a computing device10 operating in a network environment.

FIG. 2 a shows another perspective of the subjective user state dataacquisition module 102 of the computing device 10 of FIG. 1 b.

FIG. 2 b shows another perspective of the objective occurrence datasolicitation module 103 of the computing device 10 of FIG. 1 b.

FIG. 2 c shows another perspective of the objective occurrence dataacquisition module 104 of the computing device 10 of FIG. 1 b.

FIG. 2 d shows another perspective of the correlation module 106 of thecomputing device 10 of FIG. 1 b.

FIG. 2 e shows another perspective of the presentation module 108 of thecomputing device 10 of FIG. 1 b.

FIG. 2 f shows another perspective of the one or more applications 126of the computing device 10 of FIG. 1 b.

FIG. 3 is a high-level logic flowchart of a process.

FIG. 4 a is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 b is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 4 c is a high-level logic flowchart of a process depictingalternate implementations of the subjective user state data acquisitionoperation 302 of FIG. 3.

FIG. 5 a is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 304 of FIG. 3.

FIG. 5 b is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 304 of FIG. 3.

FIG. 5 c is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 304 of FIG. 3.

FIG. 5 d is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data solicitationoperation 304 of FIG. 3.

FIG. 6 a is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 306 of FIG. 3.

FIG. 6 b is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 306 of FIG. 3.

FIG. 6 c is a high-level logic flowchart of a process depictingalternate implementations of the objective occurrence data acquisitionoperation 306 of FIG. 3.

FIG. 7 a is a high-level logic flowchart of a process depictingalternate implementations of the correlation operation 308 of FIG. 3.

FIG. 7 b is a high-level logic flowchart of a process depictingalternate implementations of the correlation operation 308 of FIG. 3.

FIG. 8 is a high-level logic flowchart of another process.

FIG. 9 is a high-level logic flowchart of a process depicting alternateimplementations of the presentation operation 810 of FIG. 8.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here.

A recent trend that is becoming increasingly popular in thecomputing/communication field is to electronically record one'sfeelings, thoughts, and other aspects of the person's everyday life ontoan open diary. One place where such open diaries are maintained are atsocial networking sites commonly known as “blogs” where one or moreusers may report or post their thoughts and opinions on various topics,the latest news, and various other aspects of the users' everyday life.The process of reporting or posting blog entries is commonly referred toas blogging. Other social networking sites may allow users to updatetheir personal information via, for example, social network statusreports in which a user may report or post for others to view the lateststatus or other aspects of the user.

A more recent development in social networking is the introduction andexplosive growth of microblogs in which individuals or users (referredto as “microbloggers”) maintain open diaries at microblog websites(e.g., otherwise known as “twitters”) by continuously orsemi-continuously posting microblog entries. A microblog entry (e.g.,“tweet”) is typically a short text message that is usually not more than140 characters long. The microblog entries posted by a microblogger mayreport on any aspect of the microblogger's daily life.

The various things that are typically posted through microblog entriesmay be categorized into one of at least two possible categories. Thefirst category of things that may be reported through microblog entriesare “objective occurrences” associated with the microblogger. Objectiveoccurrences that are associated with a microblogger may be anycharacteristic, event, happening, or any other aspects associated withor are of interest to the microblogger that can be objectively reportedby the microblogger, a third party, or by a device. These things wouldinclude, for example, food, medicine, or nutraceutical intake of themicroblogger, certain physical characteristics of the microblogger suchas blood sugar level or blood pressure that can be objectively measured,daily activities of the microblogger observable by others or by adevice, external events that may not be directly related to the usersuch as the local weather or the performance of the stock market (whichthe microblogger may have an interest in), activities of others (e.g.,spouse or boss) that may directly or indirectly affect the microblogger,and so forth.

A second category of things that may be reported or posted throughmicroblogging entries include “subjective user states” of themicroblogger. Subjective user states of a microblogger include anysubjective state or status associated with the microblogger that canonly be typically reported by the microblogger (e.g., generally cannotbe reported by a third party or by a device). Such states including, forexample, the subjective mental state of the microblogger (e.g., “I amfeeling happy”), the subjective physical states of the microblogger(e.g., “my ankle is sore” or “my ankle does not hurt anymore” or “myvision is blurry”), and the subjective overall state of the microblogger(e.g., “I'm good” or “I'm well”). Note that the term “subjective overallstate” as will be used herein refers to those subjective states that maynot fit neatly into the other two categories of subjective user statesdescribed above (e.g., subjective mental states and subjective physicalstates). Although microblogs are being used to provide a wealth ofpersonal information, they have thus far been primarily limited to theiruse as a means for providing commentaries and for maintaining opendiaries.

In accordance with various embodiments, methods, systems, and computerprogram products are provided for, among other things, acquiringsubjective user state data including data indicative of at least onesubjective user state associated with a user and soliciting, in responseto the acquisition of the subjective user state data, objectiveoccurrence data including data indicating at least one objectiveoccurrence. As will be further described herein, in some embodiments,the solicitation of the objective occurrence data may, in addition to beprompted by the acquisition of the subjective user state data, may beprompted by referencing historical data. Such historical data may behistorical data that is associated with the user, associated with agroup of users, associated with a segment of the general population, orassociated with the general population.

The methods, systems, and computer program products may then correlatethe subjective user state data (e.g., data that indicate one or moresubjective user states of a user) with the objective occurrence data(e.g., data that indicate one or more objective occurrences associatedwith the user). By correlating the subjective user state data with theobjective occurrence data, a causal relationship between one or moreobjective occurrences (e.g., cause) and one or more subjective userstates (e.g., result) associated with a user (e.g., a blogger ormicroblogger) may be determined in various alternative embodiments. Forexample, determining that the last time a user ate a banana (e.g.,objective occurrence), the user felt “good” (e.g., subjective userstate) or determining whenever a user eats a banana the user always orsometimes feels good. Note that an objective occurrence does not need tooccur prior to a corresponding subjective user state but instead, mayoccur subsequent or concurrently with the incidence of the subjectiveuser state. For example, a person may become “gloomy” (e.g., subjectiveuser state) whenever it is about to rain (e.g., objective occurrence) ora person may become gloomy while (e.g., concurrently) it is raining.

As briefly described above, a “subjective user state” is in reference toany state or status associated with a user (e.g., a blogger ormicroblogger) at any moment or interval in time that only the user cantypically indicate or describe. Such states include, for example, thesubjective mental state of the user (e.g., user is feeling sad), thesubjective physical state (e.g., physical characteristic) of the userthat only the user can typically indicate (e.g., a backache or an easingof a backache as opposed to blood pressure which can be reported by ablood pressure device and/or a third party), and the subjective overallstate of the user (e.g., user is “good”). Examples of subjective mentalstates include, for example, happiness, sadness, depression, anger,frustration, elation, fear, alertness, sleepiness, and so forth.Examples of subjective physical states include, for example, thepresence, easing, or absence of pain, blurry vision, hearing loss, upsetstomach, physical exhaustion, and so forth. Subjective overall statesmay include any subjective user states that cannot be easily categorizedas a subjective mental state or as a subjective physical state. Examplesof overall states of a user that may be subjective user states include,for example, the user being good, bad, exhausted, lack of rest,wellness, and so forth.

In contrast, “objective occurrence data,” which may also be referred toas “objective context data,” may include data that indicate one or moreobjective occurrences associated with the user that occurred atparticular intervals or points in time. An objective occurrence may beany physical characteristic, event, happenings, or any other aspect thatmay be associated with or is of interest to a user that can beobjectively reported by at least a third party or a sensor device. Note,however, that such objective occurrence data does not have to beactually provided by a sensor device or by a third party, but instead,may be reported by the user himself or herself (e.g., via microblogentries). Examples of objectively reported occurrences that could beindicated by the objective occurrence data include, for example, auser's food, medicine, or nutraceutical intake, the user's location atany given point in time, a user's exercise routine, a user'sphysiological characteristics such as blood pressure, social orprofessional activities, the weather at a user's location, activitiesassociated with third parties, occurrence of external events such as theperformance of the stock market, and so forth.

The term “correlating” as will be used herein is in reference to adetermination of one or more relationships between at least twovariables. Alternatively, the term “correlating” may merely be inreference to the linking or associating of at least two variables. Inthe following exemplary embodiments, the first variable is subjectiveuser state data that represents at least one subjective user state of auser and the second variable is objective occurrence data thatrepresents at least one objective occurrence. In embodiments where thesubjective user state data includes data that indicates multiplesubjective user states, each of the subjective user states representedby the subjective user state data may be the same or similar type ofsubjective user state (e.g., user being happy) at different intervals orpoints in time. Alternatively, different types of subjective user state(e.g., user being happy and user being sad) may be represented by thesubjective user state data. Similarly, in embodiments where multipleobjective occurrences are indicated by the objective occurrence data,each of the objective occurrences may represent the same or similar typeof objective occurrence (e.g., user exercising) at different intervalsor points in time, or alternatively, different types of objectiveoccurrence (e.g., user exercising and user resting).

Various techniques may be employed for correlating subjective user statedata with objective occurrence data in various alternative embodiments.For example, in some embodiments, correlating the objective occurrencedata with the subjective user state data may be accomplished bydetermining a sequential pattern associated with at least one subjectiveuser state indicated by the subjective user state data and at least oneobjective occurrence indicated by the objective occurrence data. Inother embodiments, correlating of the objective occurrence data with thesubjective user state data may involve determining multiple sequentialpatterns associated with multiple subjective user states and multipleobjective occurrences.

A sequential pattern, as will be described herein, may define timeand/or temporal relationships between two or more events (e.g., one ormore subjective user states and one or more objective occurrences). Inorder to determine a sequential pattern, objective occurrence dataincluding data indicating at least one objective occurrence may besolicited (e.g., from a user, from one or more third party sources, orfrom one or more sensor devices) in response to an acquisition ofsubjective user state data including data indicating at least onesubjective user state.

For example, if a user reports that the user felt gloomy on a particularday (e.g., subjective user state) then a solicitation (e.g., from theuser or from a third party source such as a content provider) may bemade about the local weather (e.g., objective occurrence). Suchsolicitation of objective occurrence data may be prompted based, atleast in part, on the reporting of the subjective user state and basedon historical data such as historical data that indicates or suggeststhat the user tends to get gloomy when the weather is bad (e.g., cloudy)or based on historical data that indicates that people in the generalpopulation tend to get gloomy whenever the weather is bad. In someembodiments, such historical data may indicate or define one or morehistorical sequential patterns of the user or of the general populationas they relate to subjective user states and objective occurrences.

As briefly described above, a sequential pattern may merely indicate orrepresent the temporal relationship or relationships between at leastone subjective user state and at least one objective occurrence (e.g.,whether the incidence or occurrence of the at least one subjective userstate occurred before, after, or at least partially concurrently withthe incidence of the at least one objective occurrence). In alternativeimplementations, and as will be further described herein, a sequentialpattern may indicate a more specific time relationship between theincidences of one or more subjective user states and the incidences ofone or more objective occurrences. For example, a sequential pattern mayrepresent the specific pattern of events (e.g., one or more objectiveoccurrences and one or more subjective user states) that occurs along atimeline.

The following illustrative example is provided to describe how asequential pattern associated with at least one subjective user stateand at least one objective occurrence may be determined based, at leastin part, on the temporal relationship between the incidence of the atleast one subjective user state and the incidence of the at least oneobjective occurrence in accordance with some embodiments. For theseembodiments, the determination of a sequential pattern may initiallyinvolve determining whether the incidence of the at least one subjectiveuser state occurred within some predefined time increments of theincidence of the one objective occurrence. That is, it may be possibleto infer that those subjective user states that did not occur within acertain time period from the incidence of an objective occurrence arenot related or are unlikely related to the incidence of that objectiveoccurrence.

For example, suppose a user during the course of a day eats a banana andalso has a stomach ache sometime during the course of the day. If theconsumption of the banana occurred in the early morning hours but thestomach ache did not occur until late that night, then the stomach achemay be unrelated to the consumption of the banana and may bedisregarded. On the other hand, if the stomach ache had occurred withinsome predefined time increment, such as within 2 hours of consumption ofthe banana, then it may be concluded that there is a correlation or linkbetween the stomach ache and the consumption of the banana. If so, atemporal relationship between the consumption of the banana and theoccurrence of the stomach ache may be determined. Such a temporalrelationship may be represented by a sequential pattern. Such asequential pattern may simply indicate that the stomach ache (e.g., asubjective user state) occurred after (rather than before orconcurrently) the consumption of banana (e.g., an objective occurrence).

As will be further described herein, other factors may also bereferenced and examined in order to determine a sequential pattern andwhether there is a relationship (e.g., causal relationship) between anobjective occurrence and a subjective user state. These factors mayinclude, for example, historical data (e.g., historical medical datasuch as genetic data or past history of the user or historical datarelated to the general population regarding, for example, stomach achesand bananas) as briefly described above. Alternatively, a sequentialpattern may be determined for multiple subjective user states andmultiple objective occurrences. Such a sequential pattern mayparticularly map the exact temporal or time sequencing of the variousevents (e.g., subjective user states and/or objective occurrences). Thedetermined sequential pattern may then be used to provide usefulinformation to the user and/or third parties.

The following is another illustrative example of how subjective userstate data may be correlated with objective occurrence data bydetermining multiple sequential patterns and comparing the sequentialpatterns with each other. Suppose, for example, a user such as amicroblogger reports that the user ate a banana on a Monday. Theconsumption of the banana, in this example, is a reported firstobjective occurrence associated with the user. The user then reportsthat 15 minutes after eating the banana, the user felt very happy. Thereporting of the emotional state (e.g., felt very happy) is, in thisexample, a reported first subjective user state. Thus, the reportedincidence of the first objective occurrence (e.g., eating the banana)and the reported incidence of the first subjective user state (user feltvery happy) on Monday may be represented by a first sequential pattern.

On Tuesday, the user reports that the user ate another banana (e.g., asecond objective occurrence associated with the user). The user thenreports that 20 minutes after eating the second banana, the user feltsomewhat happy (e.g., a second subjective user state). Thus, thereported incidence of the second objective occurrence (e.g., eating thesecond banana) and the reported incidence of the second subjective userstate (user felt somewhat happy) on Tuesday may be represented by asecond sequential pattern. Note that in this example, the occurrences ofthe first subjective user state and the second subjective user state maybe indicated by subjective user state data while the occurrences of thefirst objective occurrence and the second objective occurrence may beindicated by objective occurrence data.

In a slight variation of the above example, suppose the user hadforgotten to report for Tuesday the consumption of the banana but doesreport feeling somewhat happy on Tuesday. This may result in the userbeing asked, based on the reporting of the user feeling somewhat happyon Tuesday, as to whether the user ate anything prior to feelingsomewhat happy or whether the user ate a banana prior to feelingsomewhat happy. Asking of such questions may be prompted both inresponse to the reporting of the user feeling somewhat happy on Tuesdayand on referencing historical data (e.g., first sequential patternderived from Monday's consumption of banana and feeling happy). Upon theuser confirming the consumption of the banana on Tuesday, a secondsequential pattern may be determined.

In any event, by comparing the first sequential pattern with the secondsequential pattern, the subjective user state data may be correlatedwith the objective occurrence data. In some implementations, thecomparison of the first sequential pattern with the second sequentialpattern may involve trying to match the first sequential pattern withthe second sequential pattern by examining certain attributes and/ormetrics. For example, comparing the first subjective user state (e.g.,user felt very happy) of the first sequential pattern with the secondsubjective user state (e.g., user felt somewhat happy) of the secondsequential pattern to see if they at least substantially match or arecontrasting (e.g., being very happy in contrast to being slightly happyor being happy in contrast to being sad). Similarly, comparing the firstobjective occurrence (e.g., eating a banana) of the first sequentialpattern may be compared to the second objective occurrence (e.g., eatingof another banana) of the second sequential pattern to determine whetherthey at least substantially match or are contrasting.

A comparison may also be made to determine if the extent of timedifference (e.g., 15 minutes) between the first subjective user state(e.g., user being very happy) and the first objective occurrence (e.g.,user eating a banana) matches or are at least similar to the extent oftime difference (e.g., 20 minutes) between the second subjective userstate (e.g., user being somewhat happy) and the second objectiveoccurrence (e.g., user eating another banana). These comparisons may bemade in order to determine whether the first sequential pattern matchesthe second sequential pattern. A match or substantial match wouldsuggest, for example, that a subjective user state (e.g., happiness) islinked to a particular objective occurrence (e.g., consumption ofbanana).

As briefly described above, the comparison of the first sequentialpattern with the second sequential pattern may include a determinationas to whether, for example, the respective subjective user states andthe respective objective occurrences of the sequential patterns arecontrasting subjective user states and/or contrasting objectiveoccurrences. For example, suppose in the above example the user hadreported that the user had eaten a whole banana on Monday and felt veryenergetic (e.g., first subjective user state) after eating the wholebanana (e.g., first objective occurrence). Suppose that the user alsoreported that on Tuesday he ate a half a banana instead of a wholebanana and only felt slightly energetic (e.g., second subjective userstate) after eating the half banana (e.g., second objective occurrence).In this scenario, the first sequential pattern (e.g., feeling veryenergetic after eating a whole banana) may be compared to the secondsequential pattern (e.g., feeling slightly energetic after eating only ahalf of a banana) to at least determine whether the first subjectiveuser state (e.g., being very energetic) and the second subjective userstate (e.g., being slightly energetic) are contrasting subjective userstates. Another determination may also be made during the comparison todetermine whether the first objective occurrence (eating a whole banana)is in contrast with the second objective occurrence (e.g., eating a halfof a banana).

In doing so, an inference may be made that eating a whole banana insteadof eating only a half of a banana makes the user happier or eating morebanana makes the user happier. Thus, the word “contrasting” as used herewith respect to subjective user states refers to subjective user statesthat are the same type of subjective user states (e.g., the subjectiveuser states being variations of a particular type of subjective userstates such as variations of subjective mental states). Thus, forexample, the first subjective user state and the second subjective userstate in the previous illustrative example are merely variations ofsubjective mental states (e.g., happiness). Similarly, the use of theword “contrasting” as used here with respect to objective occurrencesrefers to objective states that are the same type of objectiveoccurrences (e.g., consumption of food such as banana).

As those skilled in the art will recognize, a stronger correlationbetween the subjective user state data and the objective occurrence datacould be obtained if a greater number of sequential patterns (e.g., ifthere was a third sequential pattern, a fourth sequential pattern, andso forth, that indicated that the user became happy or happier wheneverthe user ate bananas) are used as a basis for the correlation. Note thatfor ease of explanation and illustration, each of the exemplarysequential patterns to be described herein will be depicted as asequential pattern of an occurrence of a single subjective user stateand an occurrence of a single objective occurrence. However, thoseskilled in the art will recognize that a sequential pattern, as will bedescribed herein, may also be associated with occurrences of multipleobjective occurrences and/or multiple subjective user states. Forexample, suppose the user had reported that after eating a banana, hehad gulped down a can of soda. The user then reported that he becamehappy but had an upset stomach. In this example, the sequential patternassociated with this scenario will be associated with two objectiveoccurrences (e.g., eating a banana and drinking a can of soda) and twosubjective user states (e.g., user having an upset stomach and feelinghappy).

In some embodiments, and as briefly described earlier, the sequentialpatterns derived from subjective user state data and objectiveoccurrence data may be based on temporal relationships between objectiveoccurrences and subjective user states. For example, whether asubjective user state occurred before, after, or at least partiallyconcurrently with an objective occurrence. For instance, a plurality ofsequential patterns derived from subjective user state data andobjective occurrence data may indicate that a user always has a stomachache (e.g., subjective user state) after eating a banana (e.g., firstobjective occurrence).

FIGS. 1 a and 1 b illustrate an example environment in accordance withvarious embodiments. In the illustrated environment, an exemplary system100 may include at least a computing device 10 (see FIG. 1 b) that maybe employed in order to, among other things, acquire subjective userstate data 60 associated with a user 20*, solicit and acquire objectiveoccurrence data 70* in response to the acquisition of the subjectiveuser state data 60, and to correlate the subjective user state data 60with the objective occurrence data 70*. Note that in the following, “*”indicates a wildcard. Thus, user 20* may indicate a user 20 a or a user20 b of FIGS. 1 a and 1 b.

In some embodiments, the computing device 10 may be a network server inwhich case the computing device 10 may communicate with a user 20 a viaa mobile device 30 and through a wireless and/or wired network 40. Anetwork server, as will be described herein, may be in reference to aserver located at a single network site or located across multiplenetwork sites or a conglomeration of servers located at multiple networksites. The mobile device 30 may be a variety of computing/communicationdevices including, for example, a cellular phone, a personal digitalassistant (PDA), a laptop, a desktop, or other types ofcomputing/communication device that can communicate with the computingdevice 10.

In alternative embodiments, the computing device 10 may be a localcomputing device that communicates directly with a user 20 b. For theseembodiments, the computing device 10 may be any type of handheld devicesuch as a cellular telephone, a PDA, or other types ofcomputing/communication devices such as a laptop computer, a desktopcomputer, and so forth. In various embodiments, the computing device 10may be a peer-to-peer network component device. In some embodiments, thecomputing device 10 may operate via a web 2.0 construct.

In embodiments where the computing device 10 is a server, the computingdevice 10 may obtain the subjective user state data 60 indirectly from auser 20 a via a network interface 120. In alternative embodiments inwhich the computing device 10 is a local device such as a handhelddevice (e.g., cellular telephone, personal digital assistant, etc.), thesubjective user state data 60 may be directly obtained from a user 20 bvia a user interface 122. As will be further described, the computingdevice 10 may acquire the objective occurrence data 70* from one or morealternative sources.

For ease of illustration and explanation, the following systems andoperations to be described herein will be generally described in thecontext of the computing device 10 being a network server. However,those skilled in the art will recognize that these systems andoperations may also be implemented when the computing device 10 is alocal device such as a handheld device that may communicate directlywith a user 20 b.

Assuming that the computing device 10 is a server, the computing device10, in various implementations, may be configured to acquire subjectiveuser state data 60 including data indicating at least one subjectiveuser state 60 a via the mobile device 30 and through wireless and/orwired networks 40. In some implementations, the subjective user statedata 60 may further include additional data that may indicate one ormore additional subjective user states (e.g., data indicating at least asecond subjective user state 60 b).

In various embodiments, the data indicating the at least one subjectiveuser state 60 a, as well as the data indicating the at least secondsubjective user state 60 b, may be in the form of blog entries, such asmicroblog entries, status reports (e.g., social networking statusreports), electronic messages (email, text messages, instant messages,etc.) or other types of electronic messages or documents. The dataindicating the at least one subjective user state 60 a and the dataindicating the at least second subjective user state 60 b may, in someinstances, indicate the same, contrasting, or completely differentsubjective user states.

Examples of subjective user states that may be indicated by thesubjective user state data 60 include, for example, subjective mentalstates of the user 20 a (e.g., user 20 a is sad or angry), subjectivephysical states of the user 20 a (e.g., physical or physiologicalcharacteristic of the user 20 a such as the presence, absence,elevating, or easing of a stomach ache or headache), subjective overallstates of the user 20 a (e.g., user 20 a is “well”), and/or othersubjective user states that only the user 20 a can typically indicate.

The computing device 10 may also be configured to solicit objectiveoccurrence data 70* including data indicating at least one objectiveoccurrence. Such a solicitation of the objective occurrence data 70* maybe prompted in response to the acquisition of subjective user state data60 and/or in response to referencing of historical data 72 as will befurther described herein. The solicitation of objective occurrence data70* may be made through a network interface 120 or through the userinterface 122. As will be further described, the solicitation of theobjective occurrence data 70* from a source (e.g., the user 20*, one ormore third party sources, or one or more sensors 35) may be accomplishedin a number of ways depending on the specific circumstances (e.g.,whether the computing device 10 is a server or a local device andwhether the source is the user 20*, one or more third parties 50, or oneor more sensors 35). Examples of how objective occurrence data 70* couldbe solicited include, for example, transmitting via a network interface120 a request for objective occurrence data 70*, indicating via a userinterface 122 a request for objective occurrence data 70*, configuratingor activating one or more sensors 35 to collect and provide objectiveoccurrence data 70 b, and so forth.

After soliciting for the objective occurrence data 70*, the computingdevice 10 may be configured to acquire the objective occurrence data 70*from one or more sources. In various embodiments, the objectiveoccurrence data 70* acquired by the computing device 10 may include dataindicative of at least one objective occurrence associated with a user20 a (or with user 20 b in the case where the computing device 10 is alocal device). The objective occurrence data 70* may additionallyinclude data indicative of one or more additional objective occurrencesassociated with the user 20 a (or user 20 b) including data indicatingat least a second objective occurrence associated with the user 20 a (oruser 20 b). In some embodiments, objective occurrence data 70 a may beacquired from one or more third parties 50. Examples of third parties 50include, for example, other users (not depicted), a healthcare provider,a hospital, a place of employment, a content provider, and so forth.

In some embodiments, objective occurrence data 70 b may be acquired fromone or more sensors 35 that may be designed for sensing or monitoringvarious aspects associated with the user 20 a (or user 20 b). Forexample, in some implementations, the one or more sensors 35 may includea global positioning system (GPS) device for determining the location ofthe user 20 a and/or a physical activity sensor for measuring physicalactivities of the user 20 a. Examples of a physical activity sensorinclude, for example, a pedometer for measuring physical activities ofthe user 20 a. In certain implementations, the one or more sensors 35may include one or more physiological sensor devices for measuringphysiological characteristics of the user 20 a. Examples ofphysiological sensor devices include, for example, a blood pressuremonitor, a heart rate monitor, a glucometer, and so forth. In someimplementations, the one or more sensors 35 may include one or moreimage capturing devices such as a video or digital camera.

In some embodiments, objective occurrence data 70 c may be acquired fromthe user 20 a via the mobile device 30 (or from user 20 b via userinterface 122). For these embodiments, the objective occurrence data 70c may be in the form of blog entries (e.g., microblog entries), statusreports, or other types of electronic entries or messages. In variousimplementations, the objective occurrence data 70 c acquired from theuser 20 a may indicate, for example, activities (e.g., exercise or foodor medicine intake) performed by the user 20 a, certain physicalcharacteristics (e.g., blood pressure or location) associated with theuser 20 a, or other aspects associated with the user 20 a that the user20 a can report objectively. The objective occurrence data 70 c may bein the form of a text data, audio or voice data, or image data.

After acquiring the subjective user state data 60 and the objectiveoccurrence data 70*, the computing device 10 may be configured tocorrelate the acquired subjective user data 60 with the acquiredobjective occurrence data 70* by, for example, determining whether thereis a sequential relationship between the one or more subjective userstates as indicated by the acquired subjective user state data 60 andthe one or more objective occurrences indicated by the acquiredobjective occurrence data 70*.

In some embodiments, and as will be further indicated in the operationsand processes to be described herein, the computing device 10 may befurther configured to present one or more results of correlation. Invarious embodiments, the one or more correlation results 80 may bepresented to the user 20 a and/or to one or more third parties 50 invarious forms (e.g., in the form of an advisory, a warning, aprediction, and so forth). The one or more third parties 50 may be otherusers 20* such as other microbloggers, a health care provider,advertisers, and/or content providers.

As illustrated in FIG. 1 b, computing device 10 may include one or morecomponents and/or sub-modules. For instance, in various embodiments,computing device 10 may include a subjective user state data acquisitionmodule 102, an objective occurrence data solicitation module 103, anobjective occurrence data acquisition module 104, a correlation module106, a presentation module 108, a network interface 120 (e.g., networkinterface card or NIC), a user interface 122 (e.g., a display monitor, atouchscreen, a keypad or keyboard, a mouse, an audio system including amicrophone and/or speakers, an image capturing system including digitaland/or video camera, and/or other types of interface devices), one ormore applications 126 (e.g., a web 2.0 application, a voice recognitionapplication, and/or other applications), and/or memory 140, which mayinclude historical data 72.

FIG. 2 a illustrates particular implementations of the subjective userstate data acquisition module 102 of the computing device 10 of FIG. 1b. In brief, the subjective user state data acquisition module 102 maybe designed to, among other things, acquire subjective user state data60 including data indicating at least one subjective user state 60 a. Asfurther illustrated, the subjective user state data acquisition module102 may include a subjective user state data reception module 202 forreceiving the subjective user state data 60 from a user 20 a via thenetwork interface 120 (e.g., in the case where the computing device 10is a network server). Alternatively, the subjective user state datareception module 202 may receive the subjective user state data 60directly from a user 20 b (e.g., in the case where the computing device10 is a local device) via the user interface 122.

In some implementations, the subjective user state data reception module202 may further include a user interface data reception module 204and/or a network interface data reception module 206. In brief, and aswill be further described in the processes and operations to bedescribed herein, the user interface data reception module 204 may beconfigured to acquire subjective user state data 60 via a user interface122 (e.g., a display monitor, a keyboard, a touch screen, a mouse, akeypad, a microphone, a camera, and/or other interface devices) such asin the case where the computing device 10 is a local device to be useddirectly by a user 20 b. In contrast, the network interface datareception module 206 may be configured to acquire subjective user statedata 60 from a wireless and/or wired network 40 via a network interface120 (e.g., network interface card or NIC) such as in the case where thecomputing device 10 is a network server.

In various embodiments, the subjective user state data acquisitionmodule 102 may include a time data acquisition module 208 for acquiringtime and/or temporal elements associated with one or more subjectiveuser states of a user 20*. For these embodiments, the time and/ortemporal elements (e.g., time stamps, time interval indicators, and/ortemporal relationship indicators) acquired by the time data acquisitionmodule 208 may be useful for, among other things, determining one ormore sequential patterns associated with subjective user states andobjective occurrences as will be further described herein. In someimplementations, the time data acquisition module 208 may include a timestamp acquisition module 210 for acquiring (e.g., either by receiving orgenerating) one or more time stamps associated with one or moresubjective user states. In the same or different implementations, thetime data acquisition module 208 may include a time interval acquisitionmodule 212 for acquiring (e.g., either by receiving or generating)indications of one or more time intervals associated with one or moresubjective user states. In the same or different implementations, thetime data acquisition module 208 may include a temporal relationshipacquisition module 214 for acquiring, for example, indications oftemporal relationships between subjective user states and objectiveoccurrences. For example, acquiring an indication that a subjective userstate such as a stomach ache occurred before, after, or at leastpartially concurrently with incidence of an objective occurrence such aseating lunch or the time being noon.

FIG. 2 b illustrates particular implementations of the objectiveoccurrence data solicitation module 103 of the computing device 10 ofFIG. 1 b. The objective occurrence data solicitation module 103 may beconfigured or designed to solicit, in response to acquisition ofsubjective user state data 60 including data indicating at leastsubjective user state 60 a, objective occurrence data 70* including dataindicating at least one objective occurrence. The objective occurrencedata 70* to be solicited may be requested from a user 20*, from one ormore third parties 50 (e.g., third party sources such as other users(not depicted), content providers, healthcare entities includingdoctor's or dentist offices and hospitals, and so forth), or may besolicited from one or more sensors 35. The solicitation may be made via,for example, network interface 120 or via the user interface 122 in thecase where user 20 b is the source for the objective occurrence data70*.

In various embodiments, the objective occurrence data solicitationmodule 103 may be configured to solicit data indicating occurrence of atleast one objective occurrence that occurred at a specified point intime or occurred at a specified time interval. In some implementations,the solicitation of the objective occurrence data 70* by the objectiveoccurrence data solicitation module 103 may be prompted by theacquisition of subjective user state data 60 including data indicatingat least one subjective user state 60 a and/or as a result ofreferencing historical data 72 (which may be stored in memory 140).Historical data 72, in some instances, may prompt solicitation ofparticular data indicating occurrence of a particular or a particulartype of objective occurrence. In some implementations, the historicaldata 72 to be referenced may be historical data 72 indicative of a linkbetween a subjective user state type and an objective occurrence type.In the same or different implementations, the historical data 72 to bereferenced may include one or more historical sequential patternsassociated with the user 20*, a group of users, or the generalpopulation. In the same or different implementations, the historicaldata 72 to be referenced may include historical medical data associatedwith the user 20*, associated with other users, or associated with thegeneral population. The relevance of the historical data 72 with respectto the solicitation operations performed by the objective occurrencedata solicitation module 103 will be apparent in the processes andoperations to be described herein.

In order to perform the various functions described herein, theobjective occurrence data solicitation module 103 may include a networkinterface solicitation module 215, a user interface solicitation module216, a requesting module 217, a configuration module 218, and/or adirecting/instructing module 219. In brief, the network interfacesolicitation module 215 may be employed in order to solicit objectiveoccurrence data 70* via a network interface 120. The user interfacesolicitation module 216 may be employed in order to, among other things,solicit objective occurrence data 70* via user interface 122 from, forexample, a user 20 b. The requesting module 217 may be employed in orderto request the objective occurrence data 70 a and 70 b from a user 20*or from one or more third parties 50. The configuration module 218 maybe employed in order to configure one or more sensors 35 to collect andprovide objective occurrence data 70 b. The directing/instructing module219 may be employed in order to direct and/or instruct the one or moresensors 35 to collect and provide objective occurrence data 70 b.

Referring now to FIG. 2 c illustrating particular implementations of theobjective occurrence data acquisition module 104 of the computing device10 of FIG. 1 b. In various implementations, the objective occurrencedata acquisition module 104 may be configured to acquire (e.g., receivefrom a user 20*, receive from one or more third parties 50, or receivefrom one or more sensors 35) objective occurrence data 70* includingdata indicative of one or more objective occurrences that may beassociated with a user 20*. In various embodiments, the objectiveoccurrence data acquisition module 104 may include a reception module224 configured to receive objective occurrence data 70*. In someembodiments, the reception module 224 may further include an objectiveoccurrence data user interface reception module 226 for receiving, via auser interface 122, objective occurrence data 70* including dataindicating at least one objective occurrence from a user 20 b. In thesame or different embodiments, the reception module 224 may include anobjective occurrence data network interface reception module 227 forreceiving, via a network interface 120, objective occurrence dataincluding data indicating at least one objective occurrence from a user20 b, from one or more third parties 50, or from one or more sensors 35.

In various embodiments, the objective occurrence data acquisition module104 may include a time data acquisition module 228 configured to acquire(e.g., receive or generate) time and/or temporal elements associatedwith one or more objective occurrences. For these embodiments, the timeand/or temporal elements (e.g., time stamps, time intervals, and/ortemporal relationships) may be useful for determining sequentialpatterns associated with objective occurrences and subjective userstates.

In some implementations, the time data acquisition module 228 mayinclude a time stamp acquisition module 230 for acquiring (e.g., eitherby receiving or by generating) one or more time stamps associated withone or more objective occurrences associated with a user 20*. In thesame or different implementations, the time data acquisition module 228may include a time interval acquisition module 231 for acquiring (e.g.,either by receiving or generating) indications of one or more timeintervals associated with one or more objective occurrences. In the sameor different implementations, the time data acquisition module 228 mayinclude a temporal relationship acquisition module 232 for acquiringindications of temporal relationships between objective occurrences andsubjective user states (e.g., an indication that an objective occurrenceoccurred before, after, or at least partially concurrently withincidence of a subjective user state).

Turning now to FIG. 2 d illustrating particular implementations of thecorrelation module 106 of the computing device 10 of FIG. 1 b. Thecorrelation module 106 may be configured to, among other things,correlate subjective user state data 60 with objective occurrence data70* based, at least in part, on a determination of at least onesequential pattern of at least one objective occurrence and at least onesubjective user state. In various embodiments, the correlation module106 may include a sequential pattern determination module 236 configuredto determine one or more sequential patterns of one or more subjectiveuser states and one or more objective occurrences.

The sequential pattern determination module 236, in variousimplementations, may include one or more sub-modules that may facilitatein the determination of one or more sequential patterns. As depicted,the one or more sub-modules that may be included in the sequentialpattern determination module 236 may include, for example, a “withinpredefined time increment determination” module 238, a temporalrelationship determination module 239, a subjective user state andobjective occurrence time difference determination module 240, and/or ahistorical data referencing module 241. In brief, the within predefinedtime increment determination module 238 may be configured to determinewhether at least one subjective user state of a user 20* occurred withina predefined time increment from an incidence of at least one objectiveoccurrence. For example, determining whether a user 20* feeling “bad”(i.e., a subjective user state) occurred within ten hours (i.e.,predefined time increment) of eating a large chocolate sundae (i.e., anobjective occurrence). Such a process may be used in order to filter outevents that are likely not related or to facilitate in determining thestrength of correlation between subjective user state data 60 andobjective occurrence data 70*.

The temporal relationship determination module 239 may be configured todetermine the temporal relationships between one or more subjective userstates and one or more objective occurrences. For example, this mayentail determining whether a particular subjective user state (e.g.,sore back) occurred before, after, or at least partially concurrentlywith incidence of an objective occurrence (e.g., sub-freezingtemperature).

The subjective user state and objective occurrence time differencedetermination module 240 may be configured to determine the extent oftime difference between the incidence of at least one subjective userstate and the incidence of at least one objective occurrence. Forexample, determining how long after taking a particular brand ofmedication (e.g., objective occurrence) did a user 20* feel “good”(e.g., subjective user state).

The historical data referencing module 241 may be configured toreference historical data 72 in order to facilitate in determiningsequential patterns. For example, in various implementations, thehistorical data 72 that may be referenced may include, for example,general population trends (e.g., people having a tendency to have ahangover after drinking or ibuprofen being more effective than aspirinfor toothaches in the general population), medical information such asgenetic, metabolome, or proteome information related to the user20*(e.g., genetic information of the user 20* indicating that the user20* is susceptible to a particular subjective user state in response tooccurrence of a particular objective occurrence), or historicalsequential patterns such as known sequential patterns of the generalpopulation or of the user 20*(e.g., people tending to have difficultysleeping within five hours after consumption of coffee). In someinstances, such historical data 72 may be useful in associating one ormore subjective user states with one or more objective occurrences.

In some embodiments, the correlation module 106 may include a sequentialpattern comparison module 242. As will be further described herein, thesequential pattern comparison module 242 may be configured to comparetwo or more sequential patterns with each other to determine, forexample, whether the sequential patterns at least substantially matcheach other or to determine whether the sequential patterns arecontrasting sequential patterns.

As depicted in FIG. 2 d, in various implementations, the sequentialpattern comparison module 242 may further include one or moresub-modules that may be employed in order to, for example, facilitate inthe comparison of different sequential patterns. For example, in variousimplementations, the sequential pattern comparison module 242 mayinclude one or more of a subjective user state equivalence determinationmodule 243, an objective occurrence equivalence determination module244, a subjective user state contrast determination module 245, anobjective occurrence contrast determination module 246, a temporalrelationship comparison module 247, and/or an extent of time differencecomparison module 248.

The subjective user state equivalence determination module 243 may beconfigured to determine whether subjective user states associated withdifferent sequential patterns are equivalent. For example, thesubjective user state equivalence determination module 243 may determinewhether a first subjective user state of a first sequential pattern isequivalent to a second subjective user state of a second sequentialpattern. For instance, suppose a user 20* reports that on Monday he hada stomach ache (e.g., first subjective user state) after eating at aparticular restaurant (e.g., a first objective occurrence), and supposefurther that the user 20* again reports having a stomach ache (e.g., asecond subjective user state) after eating at the same restaurant (e.g.,a second objective occurrence) on Tuesday, then the subjective userstate equivalence determination module 243 may be employed in order tocompare the first subjective user state (e.g., stomach ache) with thesecond subjective user state (e.g., stomach ache) to determine whetherthey are equivalent.

In contrast, the objective occurrence equivalence determination module244 may be configured to determine whether objective occurrences ofdifferent sequential patterns are equivalent. For example, the objectiveoccurrence equivalence determination module 244 may determine whether afirst objective occurrence of a first sequential pattern is equivalentto a second objective occurrence of a second sequential pattern. Forinstance, for the above example the objective occurrence equivalencedetermination module 244 may compare eating at the particular restauranton Monday (e.g., first objective occurrence) with eating at the samerestaurant on Tuesday (e.g., second objective occurrence) in order todetermine whether the first objective occurrence is equivalent to thesecond objective occurrence.

In some implementations, the sequential pattern comparison module 242may include a subjective user state contrast determination module 245that may be configured to determine whether subjective user statesassociated with different sequential patterns are contrasting subjectiveuser states. For example, the subjective user state contrastdetermination module 245 may determine whether a first subjective userstate of a first sequential pattern is a contrasting subjective userstate from a second subjective user state of a second sequentialpattern. To illustrate, suppose a user 20* reports that he felt very“good” (e.g., first subjective user state) after jogging for an hour(e.g., first objective occurrence) on Monday, but reports that he felt“bad” (e.g., second subjective user state) when he did not exercise(e.g., second objective occurrence) on Tuesday, then the subjective userstate contrast determination module 245 may compare the first subjectiveuser state (e.g., feeling good) with the second subjective user state(e.g., feeling bad) to determine that they are contrasting subjectiveuser states.

In some implementations, the sequential pattern comparison module 242may include an objective occurrence contrast determination module 246that may be configured to determine whether objective occurrences ofdifferent sequential patterns are contrasting objective occurrences. Forexample, the objective occurrence contrast determination module 246 maydetermine whether a first objective occurrence of a first sequentialpattern is a contrasting objective occurrence from a second objectiveoccurrence of a second sequential pattern. For instance, for the aboveexample, the objective occurrence contrast determination module 246 maycompare the “jogging” on Monday (e.g., first objective occurrence) withthe “no jogging” on Tuesday (e.g., second objective occurrence) in orderto determine whether the first objective occurrence is a contrastingobjective occurrence from the second objective occurrence. Based on thecontrast determination, an inference may be made that the user 20* mayfeel better by jogging rather than by not jogging at all.

In some embodiments, the sequential pattern comparison module 242 mayinclude a temporal relationship comparison module 247 that may beconfigured to make comparisons between different temporal relationshipsof different sequential patterns. For example, the temporal relationshipcomparison module 247 may compare a first temporal relationship betweena first subjective user state and a first objective occurrence of afirst sequential pattern with a second temporal relationship between asecond subjective user state and a second objective occurrence of asecond sequential pattern in order to determine whether the firsttemporal relationship at least substantially matches the second temporalrelationship.

For example, suppose in the above example the user 20* eating at theparticular restaurant (e.g., first objective occurrence) and thesubsequent stomach ache (e.g., first subjective user state) on Mondayrepresents a first sequential pattern while the user 20* eating at thesame restaurant (e.g., second objective occurrence) and the subsequentstomach ache (e.g., second subjective user state) on Tuesday representsa second sequential pattern. In this example, the occurrence of thestomach ache after (rather than before or concurrently) eating at theparticular restaurant on Monday represents a first temporal relationshipassociated with the first sequential pattern while the occurrence of asecond stomach ache after (rather than before or concurrently) eating atthe same restaurant on Tuesday represents a second temporal relationshipassociated with the second sequential pattern. Under such circumstances,the temporal relationship comparison module 247 may compare the firsttemporal relationship to the second temporal relationship in order todetermine whether the first temporal relationship and the secondtemporal relationship at least substantially match (e.g., stomachachesin both temporal relationships occurring after eating at therestaurant). Such a match may result in the inference that a stomachache is associated with eating at the particular restaurant.

In some implementations, the sequential pattern comparison module 242may include an extent of time difference comparison module 248 that maybe configured to compare the extent of time differences betweenincidences of subjective user states and incidences of objectiveoccurrences of different sequential patterns. For example, the extent oftime difference comparison module 248 may compare the extent of timedifference between incidence of a first subjective user state andincidence of a first objective occurrence of a first sequential patternwith the extent of time difference between incidence of a secondsubjective user state and incidence of a second objective occurrence ofa second sequential pattern. In some implementations, the comparisonsmay be made in order to determine that the extent of time differences ofthe different sequential patterns at least substantially or proximatelymatch.

In some embodiments, the correlation module 106 may include a strengthof correlation determination module 250 for determining a strength ofcorrelation between subjective user state data 60 and objectiveoccurrence data 70* associated with a user 20*. In some implementations,the strength of correlation may be determined based, at least in part,on the results provided by the other sub-modules of the correlationmodule 106 (e.g., the sequential pattern determination module 236, thesequential pattern comparison module 242, and their sub-modules).

FIG. 2 e illustrates particular implementations of the presentationmodule 108 of the computing device 10 of FIG. 1 b. In variousimplementations, the presentation module 108 may be configured topresent, for example, one or more results of the correlation operationsperformed by the correlation module 106. The one or more results may bepresented in different ways in various alternative embodiments. Forexample, in some implementations, the presentation of the one or moreresults may entail the presentation module 108 presenting to the user20*(or some other third party 50) an indication of a sequentialrelationship between a subjective user state and an objective occurrenceassociated with the user 20*(e.g., “whenever you eat a banana, you havea stomach ache). In alternative implementations, other ways ofpresenting the results of the correlation may be employed. For example,in various alternative implementations, a notification may be providedto notify past tendencies or patterns associated with a user 20*. Insome implementations, a notification of a possible future outcome may beprovided. In other implementations, a recommendation for a future courseof action based on past patterns may be provided. These and other waysof presenting the correlation results will be described in the processesand operations to be described herein.

In various implementations, the presentation module 108 may include anetwork interface transmission module 252 for transmitting one or moreresults of the correlation performed by the correlation module 106 vianetwork interface 120. For example, in the case where the computingdevice 10 is a server, the network interface transmission module 252 maybe configured to transmit to the user 20 a or a third party 50 the oneor more results of the correlation performed by the correlation module106 via a network interface 120.

In the same or different implementations, the presentation module 108may include a user interface indication module 254 for indicating theone or more results of the correlation operations performed by thecorrelation module 106 via a user interface 122. For example, in thecase where the computing device 10 is a local device, the user interfaceindication module 254 may be configured to indicate to a user 20 b theone or more results of the correlation performed by the correlationmodule 106 via a user interface 122 (e.g., a display monitor, atouchscreen, an audio system including at least a speaker, and/or otherinterface devices).

The presentation module 108 may further include one or more sub-modulesto present the one or more results of the correlation operationsperformed by the correlation module 106 in different forms. For example,in some implementations, the presentation module 108 may include asequential relationship presentation module 256 configured to present anindication of a sequential relationship between at least one subjectiveuser state of a user 20* and at least one objective occurrence. In thesame or different implementations, the presentation module 108 mayinclude a prediction presentation module 258 configured to present aprediction of a future subjective user state of a user 20* resultingfrom a future objective occurrence associated with the user 20*. In thesame or different implementations, the prediction presentation module258 may also be designed to present a prediction of a future subjectiveuser state of a user 20* resulting from a past objective occurrenceassociated with the user 20*. In some implementations, the presentationmodule 108 may include a past presentation module 260 that is designedto present a past subjective user state of a user 20* in connection witha past objective occurrence associated with the user 20*.

In some implementations, the presentation module 108 may include arecommendation module 262 that is configured to present a recommendationfor a future action based, at least in part, on the results of acorrelation of subjective user state data 60 with objective occurrencedata 70* performed by the correlation module 106. In certainimplementations, the recommendation module 262 may further include ajustification module 264 for presenting a justification for therecommendation presented by the recommendation module 262. In someimplementations, the presentation module 108 may include a strength ofcorrelation presentation module 266 for presenting an indication of astrength of correlation between subjective user state data 60 andobjective occurrence data 70*.

In various embodiments, the computing device 10 may include a networkinterface 120 that may facilitate in communicating with a user 20 a, oneor more sensors 35, and/or one or more third parties 50. For example, inembodiments where the computing device 10 is a server, the computingdevice 10 may include a network interface 120 that may be configured toreceive from the user 20 a subjective user state data 60. In someembodiments, objective occurrence data 70 a, 70 b, and/or 70 c may alsobe received through the network interface 120. Examples of a networkinterface 120 includes, for example, a network interface card (NIC).

The computing device 10, in various embodiments, may also include amemory 140 for storing various data. For example, in some embodiments,memory 140 may be employed in order to store historical data 72. In someimplementations, the historical data 72 may include historicalsubjective user state data of a user 20* that may indicate one or morepast subjective user states of the user 20* and historical objectiveoccurrence data that may indicate one or more past objectiveoccurrences. In same or different implementations, the historical data72 may include historical medical data of a user 20* (e.g., genetic,metoblome, proteome information), population trends, historicalsequential patterns derived from general population, and so forth.

In various embodiments, the computing device 10 may include a userinterface 122 to communicate directly with a user 20 b. For example, inembodiments in which the computing device 10 is a local device such as ahandheld device (e.g., cellular telephone, PDA, and so forth), the userinterface 122 may be configured to directly receive from the user 20 bsubjective user state data 60. The user interface 122 may include, forexample, one or more of a display monitor, a touch screen, a key board,a key pad, a mouse, an audio system, an imaging system including adigital or video camera, and/or other user interface devices.

FIG. 2 e illustrates particular implementations of the one or moreapplications 126 of FIG. 1 b. For these implementations, the one or moreapplications 126 may include, for example, one or more communicationapplications 267 such as a text messaging application and/or an audiomessaging application including a voice recognition system application.In some implementations, the one or more applications 126 may include aweb 2.0 application 268 to facilitate communication via, for example,the World Wide Web. The functional roles of the various components,modules, and sub-modules of the computing device 10 presented thus farwill be described in greater detail with respect to the processes andoperations to be described herein. Note that the subjective user statedata 60 may be in a variety of forms including, for example, textmessages (e.g., blog entries, microblog entries, instant messages, textemail messages, and so forth), audio messages, and/or images (e.g., animage capturing user's facial expression or gestures).

FIG. 3 illustrates an operational flow 300 representing exampleoperations related to, among other things, acquisition and correlationof subjective user state data 60 and objective occurrence data 70* inaccordance with various embodiments. In some embodiments, theoperational flow 300 may be executed by, for example, the computingdevice 10 of FIG. 1 b.

In FIG. 3 and in the following figures that include various examples ofoperational flows, discussions and explanations may be provided withrespect to the above-described exemplary environment of FIGS. 1 a and 1b, and/or with respect to other examples (e.g., as provided in FIGS. 2a-2 f) and contexts. However, it should be understood that theoperational flows may be executed in a number of other environments andcontexts, and/or in modified versions of FIGS. 1 a, 1 b, and 2 a-2 f.Also, although the various operational flows are presented in thesequence(s) illustrated, it should be understood that the variousoperations may be performed in other orders than those which areillustrated, or may be performed concurrently.

Further, in FIG. 3 and in following figures, various operations may bedepicted in a box-within-a-box manner. Such depictions may indicate thatan operation in an internal box may comprise an optional exampleembodiment of the operational step illustrated in one or more externalboxes. However, it should be understood that internal box operations maybe viewed as independent operations separate from any associatedexternal boxes and may be performed in any sequence with respect to allother illustrated operations, or may be performed concurrently.

In any event, after a start operation, the operational flow 300 may moveto a subjective user state data acquisition operation 302 for acquiringsubjective user state data including data indicating at least onesubjective user state associated with a user. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 of FIG. 1 b acquiring (e.g., receiving via network interface120 or via user interface 122) subjective user state data 60 includingdata indicating at least one subjective user state 60 a (e.g., asubjective mental state, a subjective physical state, or a subjectiveoverall state) associated with a user 20*.

Operational flow 300 may also include an objective occurrence datasolicitation operation 304 for soliciting, in response to theacquisition of the subjective user state data, objective occurrence dataincluding data indicating occurrence of at least one objectiveoccurrence. For instance, the objective occurrence data solicitationmodule 103 of the computing device 10 soliciting (e.g., from the user20*, from one or more third parties 50, or from one or more sensors 35),in response to the acquisition of the subjective user state data 60,objective occurrence data 70* including data indicating occurrence of atleast one objective occurrence 60 a (e.g., ingestion of a food,medicine, or nutraceutical). Note that the solicitation of the objectiveoccurrence data as described above does not necessarily mean, althoughit may in some cases, to solicitation of particular data that indicatesoccurrence of a particular or particular type of objective occurrence.

The term “soliciting” as used above may be in reference to direct orindirect solicitation of objective occurrence data 70* from one or moresources (e.g., user 20*, one or more sensors 35, or one or more thirdparties 50). For example, if the computing device 10 is a server, thenthe computing device 10 may indirectly solicit the objective occurrencedata 70* from, for example, a user 20 a by transmitting the solicitation(e.g., a request or inquiry) for the objective occurrence data 70* tothe mobile device 30, which may then actually solicit the objectiveoccurrence data 70* from the user 20 a.

Operational flow 300 may further include an objective occurrence dataacquisition operation 306 for acquiring the objective occurrence data.For instance, the objective occurrence data acquisition module 104 ofthe computing device 10 acquiring (e.g., receiving via user interface122 or via the network interface 120) the objective occurrence data 70*.

Finally, operational flow 300 may include a correlation operation 308for correlating the subjective user state data with the objectiveoccurrence data. For instance, the correlation module 106 of thecomputing device 10 correlating the subjective user state data 60 withthe objective occurrence data 70* by determining, for example, at leastone sequential pattern (e.g., time sequential pattern) associated withthe at least one subjective user state (e.g., user feeling “tired”) andthe at least one objective occurrence (e.g., high blood sugar level).

In various implementations, the subjective user state data acquisitionoperation 302 may include one or more additional operations asillustrated in FIGS. 4 a, 4 b, and 4 c. For example, in someimplementations the subjective user state data acquisition operation 302may include a reception operation 402 for receiving the subjective userstate data as depicted in FIGS. 4 a and 4 b. For instance, thesubjective user state data reception module 202 (see FIG. 2 a) of thecomputing device 10 receiving (e.g., via network interface 120 or viathe user interface 122) the subjective user state data 60.

The reception operation 402 may, in turn, further include one or moreadditional operations. For example, in some implementations, thereception operation 402 may include an operation 404 for receiving thesubjective user state data via a user interface as depicted in FIG. 4 a.For instance, the user interface data reception module 204 (see FIG. 2a) of the computing device 10 receiving the subjective user state data60 via a user interface 122 (e.g., a keypad, a keyboard, a touchscreen,a mouse, an audio system including a microphone, an image capturingsystem including a video or digital camera, and/or other interfacedevices).

In some implementations, the reception operation 402 may include anoperation 406 for receiving the subjective user state data via a networkinterface as depicted in FIG. 4 a. For instance, the network interfacedata reception module 206 of the computing device 10 receiving thesubjective user state data 60 from a wireless and/or wired network 40via a network interface 120 (e.g., a NIC).

In various implementations, operation 406 may further include one ormore additional operations. For example, in some implementationsoperation 406 may include an operation 408 for receiving data indicatingthe at least one subjective user state via an electronic messagegenerated by the user as depicted in FIG. 4 a. For instance, the networkinterface data reception module 206 of the computing device 10 receivingdata indicating the at least one subjective user state 60 a (e.g.,subjective mental state such as feelings of happiness, sadness, anger,frustration, mental fatigue, drowsiness, alertness, and so forth) via anelectronic message (e.g., email, IM, or text message) generated by theuser 20 a.

In some implementations, operation 406 may include an operation 410 forreceiving data indicating the at least one subjective user state via ablog entry generated by the user as depicted in FIG. 4 a. For instance,the network interface data reception module 206 of the computing device10 receiving data indicating the at least one subjective user state 60 a(e.g., subjective physical state such as physical exhaustion, physicalpain such as back pain or toothache, upset stomach, blurry vision, andso forth) via a blog entry such as a microblog entry generated by theuser 20 a.

In some implementations, operation 406 may include an operation 412 forreceiving data indicating the at least one subjective user state via astatus report generated by the user as depicted in FIG. 4 a. Forinstance, the network interface data reception module 206 of thecomputing device 10 receiving data indicating the at least onesubjective user state 60 a (e.g., subjective overall state of the user20* such as good, bad, well, exhausted, and so forth) via a statusreport (e.g., social network site status report) generated by the user20 a.

In some implementations, the reception operation 402 may include anoperation 414 for receiving subjective user state data including dataindicating at least one subjective user state specified by a selectionmade by the user, the selection being a selection of a subjective userstate from a plurality of indicated alternative subjective user statesas depicted in FIG. 4 a. For instance, the subjective user state datareception module 202 of the computing device 10 receiving subjectiveuser state data 60 including data indicating at least one subjectiveuser state 60 a specified by a selection (e.g., via mobile device 30 orvia user interface 122) made by the user 20*, the selection being aselection of a subjective user state from a plurality of indicatedalternative subjective user states (e.g., as provided by the mobiledevice 30 or by the user interface 122). For example, the user 20* maybe given the option of selecting one or more subjective user states froma list of identified subjective user states that are shown or indicatedby the mobile device 30 or by the user interface 122.

Operation 414 may include one or more additional operations in variousalternative implementations. For example, in some implementations,operation 414 may include an operation 416 for receiving subjective userstate data including data indicating at least one subjective user statespecified by a selection made by the user, the selection being aselection of a subjective user state from at least two indicatedalternative contrasting subjective user states as depicted in FIG. 4 a.For instance, the subjective user state data reception module 202 of thecomputing device 10 receiving subjective user state data 60 includingdata indicating at least one subjective user state 60 a specified (e.g.,via the mobile device 30 or via the user interface 122) by a selectionmade by the user 20*, the selection being a selection of a subjectiveuser state from at least two indicated alternative contrastingsubjective user states (e.g., is user in pain or not in pain?, oralternatively, is user in extreme pain, user in moderate pain, or usernot in pain?).

In some implementations, operation 414 may include an operation 417 forreceiving the selection via a network interface as depicted in FIG. 4 a.For instance, the network interface data reception module 206 of thecomputing device 10 receiving the selection of a subjective user state(e.g., a subjective mental state, a subjective physical state, or asubjective overall state) via a network interface 120.

In some implementations, operation 414 may include an operation 418 forreceiving the selection via user interface as depicted in FIG. 4 a. Forinstance, the user interface data reception module 204 of the computingdevice 10 receiving the selection of a subjective user state (e.g., asubjective mental state, a subjective physical state, or a subjectiveoverall state) via a user interface 122.

In some implementations, the reception operation 402 may include anoperation 420 for receiving data indicating at least one subjective userstate associated with the user that was obtained based, at least inpart, on a text entry provided by the user as depicted in FIG. 4 b. Forinstance, the subjective user state data reception module 202 of thecomputing device 10 receiving data indicating at least one subjectiveuser state 60 a (e.g., a subjective mental state, a subjective physicalstate, or a subjective overall state) associated with the user 20* thatwas obtained based, at least in part, on a text entry provided by theuser 20*(e.g., text data provided by the user 20* via the mobile device30 or via the user interface 122).

In some implementations, the reception operation 402 may include anoperation 422 for receiving data indicating at least one subjective userstate associated with the user that was obtained based, at least inpart, on an audio entry provided by the user as depicted in FIG. 4 b.For instance, the subjective user state data reception module 202 of thecomputing device 10 receiving data indicating at least one subjectiveuser state 60 a (e.g., a subjective mental state, a subjective physicalstate, or a subjective overall state) associated with the user 20* thatwas obtained based, at least in part, on an audio entry provided by theuser 20*(e.g., audio recording made via the mobile device 30 or via theuser interface 122).

In some implementations, the reception operation 402 may include anoperation 424 for receiving data indicating at least one subjective userstate associated with the user that was obtained based, at least inpart, on an image entry provided by the user as depicted in FIG. 4 b.For instance, the subjective user state data reception module 202 of thecomputing device 10 receiving data indicating at least one subjectiveuser state 60 a (e.g., a subjective mental state, a subjective physicalstate, or a subjective overall state) associated with the user 20* thatwas obtained based, at least in part, on an image entry provided by theuser 20*(e.g., one or more images recorded via the mobile device 30 orvia the user interface 122).

Operation 424 may further include one or more additional operations invarious alternative implementations. For example, in someimplementations, operation 424 may include an operation 426 forreceiving data indicating at least one subjective user state associatedwith the user that was obtained based, at least in part, on an imageentry showing a gesture made by the user as depicted in FIG. 4 b. Forinstance, the subjective user state data reception module 202 of thecomputing device 10 receiving data indicating at least one subjectiveuser state 60 a (e.g., a subjective user state such as “user is good” or“user is not good”) associated with the user 20* that was obtainedbased, at least in part, on an image entry showing a gesture (e.g., athumb up or a thumb down) made by the user 20*.

In some implementations, operation 424 may include an operation 428 forreceiving data indicating at least one subjective user state associatedwith the user that was obtained based, at least in part, on an imageentry showing an expression made by the user as depicted in FIG. 4 b.For instance, the subjective user state data reception module 202 of thecomputing device 10 receiving data indicating at least one subjectiveuser state 60 a (e.g., a subjective mental state such as happiness orsadness) associated with the user 20* that was obtained based, at leastin part, on an image entry showing an expression (e.g., a smile or afrown expression) made by the user 20*.

In some implementations, the reception operation 402 may include anoperation 430 for receiving data indicating at least one subjective userstate associated with the user that was obtained based, at least inpart, on data provided through user interaction with a user interface asdepicted in FIG. 4 b. For instance, the subjective user state datareception module 202 of the computing device 10 receiving dataindicating at least one subjective user state 60 a associated with theuser 20* that was obtained based, at least in part, on data providedthrough user interaction (e.g., user 20* selecting one subjective userstate from a plurality of alternative subjective user states) with auser interface 122 (e.g., keypad, a touchscreen, a microphone, and soforth) of the computing device 10 or with a user interface of the mobiledevice 30.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 432 for acquiring data indicatingat least one subjective mental state of the user as depicted in FIG. 4b. For instance, the subjective user state data acquisition module 102of the computing device 10 acquiring (e.g., via network interface 120 orvia user interface 122) data indicating at least one subjective mentalstate (e.g., sadness, happiness, alertness or lack of alertness, anger,frustration, envy, hatred, disgust, and so forth) of the user 20*.

In some implementations, operation 432 may further include an operation434 for acquiring data indicating at least a level of the one subjectivemental state of the user as depicted in FIG. 4 b. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 acquiring data indicating at least a level of the onesubjective mental state (e.g., extreme sadness or slight sadness) of theuser 20*.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 436 for acquiring data indicatingat least one subjective physical state of the user as depicted in FIG. 4b. For instance, the subjective user state data acquisition module 102of the computing device 10 acquiring (e.g., via network interface 120 orvia user interface 122) data indicating at least one subjective physicalstate (e.g., blurry vision, physical pain such as backache or headache,upset stomach, physical exhaustion, and so forth) of the user 20*.

In some implementations, operation 436 may further include an operation438 for acquiring data indicating at least a level of the one subjectivephysical state of the user as depicted in FIG. 4 b. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 acquiring data indicating at least a level of the onesubjective physical state (e.g., a slight headache or a severe headache)of the user 20*.

In various implementations, the subjective user state data acquisitionoperation 302 may include an operation 440 for acquiring data indicatingat least one subjective overall state of the user as depicted in FIG. 4c. For instance, the subjective user state data acquisition module 102of the computing device 10 acquiring (e.g., via network interface 120 orvia user interface 122) data indicating at least one subjective overallstate (e.g., good, bad, wellness, hangover, fatigue, nausea, and soforth) of the user 20*. Note that a subjective overall state, as usedherein, may be in reference to any subjective user state that may notfit neatly into the categories of subjective mental state or subjectivephysical state.

In some implementations, operation 440 may further include an operation442 for acquiring data indicating at least a level of the one subjectiveoverall state of the user as depicted in FIG. 4 c. For instance, thesubjective user state data acquisition module 102 of the computingdevice 10 acquiring data indicating at least a level of the onesubjective overall state (e.g., a very bad hangover) of the user 20*.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 444 for acquiring a time stampassociated with occurrence of the at least one subjective user state asdepicted in FIG. 4 c. For instance, the time stamp acquisition module210 (see FIG. 2 a) of the computing device 10 acquiring (e.g., receivingvia the network interface 120 or via the user interface 122 as providedby the user 20* or by automatically or self generating) a time stamp(e.g., 10 PM Aug. 4, 2009) associated with occurrence of the at leastone subjective user state.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 446 for acquiring an indicationof a time interval associated with occurrence of the at least onesubjective user state as depicted in FIG. 4 c. For instance, the timeinterval acquisition module 212 of the computing device 10 acquiring(e.g., via the network interface 120 or via the user interface 122 asprovided by the user 20* or by automatically generating) an indicationof a time interval (e.g., 8 AM to 10 AM Jul. 24, 2009) associated withoccurrence of the at least one subjective user state.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 448 for acquiring an indicationof a temporal relationship between occurrence of the at least onesubjective user state and occurrence of the at least one objectiveoccurrence as depicted in FIG. 4 c. For instance, the temporalrelationship acquisition module 214 of the computing device 10 acquiring(e.g., via the network interface 120 or via the user interface 122 asprovided by the user 20* or by automatically generating) an indicationof a temporal relationship (e.g., before, after, or at least partiallyconcurrently) between occurrence of the at least one subjective userstate (e.g., easing of a headache) and occurrence of at least oneobjective occurrence (e.g., ingestion of aspirin). For example,acquiring an indication that a user's headache eased after taking anaspirin.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 450 for acquiring the subjectiveuser state data at a server as depicted in FIG. 4 c. For instance, whenthe computing device 10 is a network server and is acquiring thesubjective user state data 60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 452 for acquiring the subjectiveuser state data at a handheld device as depicted in FIG. 4 c. Forinstance, when the computing device 10 is a handheld device such as amobile phone or a PDA and is acquiring the subjective user state data60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 454 for acquiring the subjectiveuser state data at a peer-to-peer network component device as depictedin FIG. 4 c. For instance, when the computing device 10 is apeer-to-peer network component device and is acquiring the subjectiveuser state data 60.

In some implementations the subjective user state data acquisitionoperation 302 may include an operation 456 for acquiring the subjectiveuser state data via a Web 2.0 construct as depicted in FIG. 4 c. Forinstance, when the computing device 10 employs a Web 2.0 application inorder to acquire the subjective user state data 60.

Referring back to FIG. 3, the objective occurrence data solicitationoperation 304 in various embodiments may include one or more additionaloperations as illustrated in FIGS. 5 a to 5 d. For example, in someimplementations, the objective occurrence data solicitation operation304 may include an operation 500 for soliciting from the user the dataindicating occurrence of at least one objective occurrence as depictedin FIGS. 5 a and 5 b. For instance, the objective occurrence datasolicitation module 103 of the computing device 10 soliciting (e.g., vianetwork interface 120 or via user interface 122) from the user 20* thedata indicating occurrence of at least one objective occurrence (e.g.,ingestion of a food item, medicine, or nutraceutical, exercise or otheractivities performed by the user 20* or by others, or external eventssuch as weather or performance of the stock market).

Operation 500 may also further include one or more additionaloperations. For example, in some implementations, operation 500 mayinclude an operation 502 for soliciting the data indicating anoccurrence of at least one objective occurrence via user interface asdepicted in FIG. 5 a. For instance, the user interface solicitationmodule 216 of the computing device 10 soliciting (e.g., requesting orseeking from the user 20 b) the data indicating an occurrence of atleast one objective occurrence (e.g., ingestion of a food item, amedicine, or a nutraceutical by the user 20 b) via user interface 122.

Operation 502, in turn, may include one or more additional operations.For example, in some implementations, operation 502 may include anoperation 504 for soliciting the data indicating an occurrence of atleast one objective occurrence through at least one of a display monitoror a touchscreen as depicted in FIG. 5 a. For instance, the userinterface solicitation module 216 of the computing device 10 soliciting(e.g., requesting or seeking from the user 20 b) the data indicating anoccurrence of at least one objective occurrence (e.g., social, work, orexercise activity performed by the user 20 b or by a third party 50)through at least one of a display monitor or a touchscreen.

In some implementations, operation 502 may include an operation 506 forsoliciting the data indicating an occurrence of at least one objectiveoccurrence through at least an audio system as depicted in FIG. 5 a. Forinstance, the user interface solicitation module 216 of the computingdevice 10 soliciting the data indicating an occurrence of at least oneobjective occurrence (e.g., activity performed by a third party 50 or aphysical characteristic of the user 20 b such as blood pressure) throughat least an audio system (e.g., a speaker system).

In various implementations, operation 500 may include an operation 508for soliciting the data indicating an occurrence of at least oneobjective occurrence via a network interface as depicted in FIG. 5 a.For instance, the network interface solicitation module 215 (see FIG. 2b) soliciting (e.g., requesting or seeking from the user 20 a, one ormore third parties 50, or from one or more sensors 35) the dataindicating an occurrence of at least one objective occurrence (e.g., anexternal event such as local weather or the location of the user 20*)via a network interface 120.

In some implementations, operation 500 may include an operation 510 forrequesting the user to confirm occurrence of at least one objectiveoccurrence as depicted in FIG. 5 a. For instance, the requesting module217 (see FIG. 2 b) of the computing device 10 requesting (e.g.,transmitting a request or an inquiry via the network interface 120 ordisplaying a request or an inquiry via the user interface 122) the user20* to confirm occurrence of at least one objective occurrence (e.g.,did user 20* ingest a particular type of medicine?).

In some implementations, operation 500 may include an operation 512 forrequesting the user to select at least one objective occurrence from aplurality of indicated alternative objective occurrences as depicted inFIG. 5 a. For instance, the requesting module 217 of the computingdevice 10 requesting (e.g., transmitting a request via the networkinterface 120 or displaying a request via the user interface 122) theuser 20* to select at least one objective occurrence from a plurality ofindicated alternative objective occurrences (e.g., did user ingestaspirin, ibuprofen, or acetaminophen today?). For example, the user 20*may be given the option of selecting one or more objective occurrencesfrom a list of identified objective occurrences that are shown orindicated by the mobile device 30 or by the user interface 122.

Operation 512, in various implementations, may in turn include anoperation 514 for requesting the user to select one objective occurrencefrom at least two indicated alternative contrasting objectiveoccurrences as depicted in FIG. 5 a. For instance, the requesting module217 of the computing device 10 requesting (e.g., transmitting a requestvia the network interface 120 or displaying a request via the userinterface 122) the user 20* to select one objective occurrence from atleast two indicated alternative contrasting objective occurrences (e.g.,ambient temperature being greater than or equal to 90 degrees or lessthan 90 degrees?).

In some implementations, operation 500 may include an operation 516 forrequesting the user to provide an indication of occurrence of at leastone objective occurrence with respect to occurrence of the at least onesubjective user state as depicted in FIG. 5 a. For instance, therequesting module 217 of the computing device 10 requesting (e.g., viathe network interface 120 or via the user interface 122) the user 20* toprovide an indication of occurrence of at least one objective occurrencewith respect to occurrence of the at least one subjective user state(you felt sick this morning, did you drink last night?).

In some implementations, operation 500 may include an operation 518 forrequesting the user to provide an indication of occurrence of at leastone objective occurrence associated with a particular type of objectiveoccurrences as depicted in FIG. 5 b. For instance, the requesting module217 of the computing device 10 requesting (e.g., via the networkinterface 120 or via the user interface 122) the user 20* to provide anindication of occurrence of at least one objective occurrence associatedwith a particular type of objective occurrences (e.g., what type ofexercise did you do today?)

In some implementations, operation 500 may include an operation 520 forrequesting the user to provide an indication of a time or temporalelement associated with occurrence of the at least one objectiveoccurrence as depicted in FIG. 5 b. For instance, the requesting module217 of the computing device 10 requesting (e.g., via the networkinterface 120 or via the user interface 122) the user 20* to provide anindication of a time or temporal element associated with occurrence ofthe at least one objective occurrence (e.g., what time did you exerciseor did you exercise before or after eating lunch?).

Operation 520 in various implementations may further include one or moreadditional operations. For example, in some implementations, operation520 may include an operation 522 for requesting the user to provide anindication of a point in time associated with the occurrence of the atleast one objective occurrence as depicted in FIG. 5 b. For instance,the requesting module 217 of the computing device 10 requesting (e.g.,via the network interface 120 or via the user interface 122) the user20* to provide an indication of a point in time associated with theoccurrence of the at least one objective occurrence (e.g., at what timeof the day did you ingest the aspirin?)

In some implementations, operation 520 may include an operation 524 forrequesting the user to provide an indication of a time intervalassociated with the occurrence of the at least one objective occurrenceas depicted in FIG. 5 b. For instance, the requesting module 217 of thecomputing device 10 requesting (e.g., via the network interface 120 orvia the user interface 122) the user 20* to provide an indication of atime interval associated with the occurrence of the at least oneobjective occurrence (e.g., from what time to what time did you takeyour walk?).

In some implementations, operation 500 may include an operation 526 forrequesting the user to provide an indication of temporal relationshipbetween occurrence of the at least one objective occurrence andoccurrence of the at least one subjective user state as depicted in FIG.5 b. For instance, the requesting module 217 of the computing device 10requesting (e.g., via the network interface 120 or via the userinterface 122) the user 20* to provide an indication of temporalrelationship between occurrence of the at least one objective occurrenceand occurrence of the at least one subjective user state (e.g., did youingest the ibuprofen before or after your headache went away?).

In various implementations, the solicitation operation 304 of FIG. 3 mayinclude an operation 528 for soliciting from one or more third partysources the data indicating occurrence of at least one objectiveoccurrence as depicted in FIG. 5 c. For instance, the objectiveoccurrence data solicitation module 103 of the computing device 10soliciting from one or more third party sources (e.g., a fitness gym, ahealthcare facility, another user, a content provider, or other thirdparty source) the data indicating occurrence of at least one objectiveoccurrence (e.g., weather, medical treatment, user 20* or third partyactivity, and so forth).

Operation 528 may, in turn, include one or more additional operations invarious alternative implementations. For example, in someimplementations, operation 528 may include an operation 530 forrequesting from one or more other users the data indicating occurrenceof at least one objective occurrence as depicted in FIG. 5 c. Forinstance, the requesting module 217 of the computing device 10requesting (e.g., via wireless and/or wired network 40) from one or moreother users (e.g., other microbloggers) the data indicating occurrenceof at least one objective occurrence (e.g., user activities observed bythe one or more other users or the one or more other users' activities).

In some implementations, operation 528 may include an operation 532 forrequesting from one or more healthcare entities the data indicatingoccurrence of at least one objective occurrence as depicted in FIG. 5 c.For instance, the requesting module 217 of the computing device 10requesting (e.g., via an electronic message) from one or more healthcareentities (e.g., physician's or dental office, medical clinic, hospital,and so forth) the data indicating occurrence of at least one objectiveoccurrence (e.g., occurrence of a medical or dental treatment).

In some implementations, operation 528 may include an operation 533 forrequesting from one or more content providers the data indicatingoccurrence of at least one objective occurrence as depicted in FIG. 5 c.For instance, the requesting module 217 of the computing device 10requesting (e.g., via a network interface 120) from one or more contentproviders the data indicating occurrence of at least one objectiveoccurrence (e.g., weather or stock market performance).

In some implementations, operation 528 may include an operation 534 forrequesting from one or more third party sources the data indicatingoccurrence of at least one objective occurrence that occurred at aspecified point in time as depicted in FIG. 5 c. For instance, therequesting module 217 of the computing device 10 requesting (e.g., via anetwork interface 120) from one or more third party sources (e.g.,dental office) the data indicating occurrence of at least one objectiveoccurrence that occurred at a specified point in time (e.g., askingwhether the user 20* was sedated with nitrous oxide at 3 PM during adental procedure).

In some implementations, operation 528 may include an operation 535 forrequesting from one or more third party sources the data indicatingoccurrence of at least one objective occurrence that occurred during aspecified time interval as depicted in FIG. 5 c. For instance, therequesting module 217 of the computing device 10 requesting (e.g., via anetwork interface 120) from one or more third party sources (e.g.,fitness instructor or gym) the data indicating occurrence of at leastone objective occurrence that occurred during a specified time interval(e.g., did user exercise on the treadmill between 6 AM and 12 PM?).

In some implementations, the solicitation operation 304 of FIG. 3 mayinclude an operation 536 for soliciting from one or more sensors thedata indicating occurrence of at least one objective occurrence asdepicted in FIG. 5 c. For instance, the objective occurrence datasolicitation module 103 of the computing device 10 soliciting (e.g., viaa network interface 120) from one or more sensors 35 (e.g., GPS) thedata indicating occurrence of at least one objective occurrence (e.g.,user location).

Operation 536 may include, in various implementations, one or moreadditional operations. For example, in some implementations, operation536 may include an operation 538 for configuring the one or more sensorsto collect and provide the data indicating occurrence of at least oneobjective occurrence as depicted in FIG. 5 c. For instance, theconfiguration module 218 of the computing device 10 configuring the oneor more sensors 35 (e.g., blood pressure device, glucometer, GPS,pedometer, or other sensors 35) to collect and provide the dataindicating occurrence of at least one objective occurrence.

In some implementations, operation 536 may include an operation 540 fordirecting or instructing the one or more sensors to collect and providethe data indicating occurrence of at least one objective occurrence asdepicted in FIG. 5 c. For instance, the directing/instructing module 219of the computing device directing or instructing the one or more sensors35 (e.g., blood pressure device, glucometer, GPS, pedometer, or othersensors 35) to collect and provide the data indicating occurrence of atleast one objective occurrence

The solicitation operation 304 of FIG. 3, in various implementations,may include an operation 542 for soliciting the data indicatingoccurrence of at least one objective occurrence in response to theacquisition of the subjective user state data and based on historicaldata as depicted in FIG. 5 d. For instance, the objective occurrencedata solicitation module 103 of the computing device 10 being promptedto soliciting the data indicating occurrence of at least one objectiveoccurrence (e.g., asking whether the user 20* ate anything or ate achocolate sundae) in response to the acquisition of the subjective userstate data 60 (e.g., subjective user state data 60 indicating a stomachache) and based on historical data 72 (e.g., a previously determinedsequential pattern associated with the user 20* indicating that the user20* may have gotten a stomach ache after eating a chocolate sundae).

In various implementations, operation 542 may further include one ormore additional operations. For example, in some implementations,operation 542 may include an operation 544 for soliciting the dataindicating occurrence of at least one objective occurrence based, atleast in part, on one or more historical sequential patterns as depictedin FIG. 5 d. For instance, the objective occurrence data solicitationmodule 103 of the computing device 10 soliciting (e.g., via networkinterface 120 or via user interface 122) the data indicating occurrenceof at least one objective occurrence based, at least in part, onreferencing of one or more historical sequential patterns (e.g.,historical sequential patterns derived from general population or from agroup of users 20*).

In some implementations, operation 542 may include an operation 546 forsoliciting the data indicating occurrence of at least one objectiveoccurrence based, at least in part, on medical data of the user asdepicted in FIG. 5 d. For instance, the objective occurrence datasolicitation module 103 of the computing device 10 soliciting (e.g., vianetwork interface 120 or via user interface 122) the data indicatingoccurrence of at least one objective occurrence based, at least in part,on medical data of the user 20* (e.g., genetic, metabolome, or proteomedata of the user).

In some implementations, operation 542 may include an operation 547 forsoliciting the data indicating occurrence of at least one objectiveoccurrence based, at least in part, on historical data indicative of alink between a subjective user state type and an objective occurrencetype as depicted in FIG. 5 d. For instance, the objective occurrencedata solicitation module 103 of the computing device 10 soliciting(e.g., via network interface 120 or via user interface 122) the dataindicating occurrence of at least one objective occurrence (e.g., localweather) based, at least in part, on historical data 72 indicative of alink between a subjective user state type and an objective occurrencetype (e.g., link between moods of people and weather).

In some implementations, operation 542 may include an operation 548 forsoliciting the data indicating occurrence of at least one objectiveoccurrence, the soliciting prompted, at least in part, by the historicaldata as depicted in FIG. 5 d. For instance, the objective occurrencedata solicitation module 103 of the computing device 10 soliciting(e.g., via network interface 120 or via user interface 122) the dataindicating occurrence of at least one objective occurrence (e.g.,weather), the soliciting prompted, at least in part, by the historicaldata 72 (e.g., historical data 72 that indicates that the user 20* orpeople in the general population tend to be gloomy (a subjective userstate) when the weather is overcast).

In some implementations, operation 542 may include an operation 549 forsoliciting data indicating occurrence of a particular or a particulartype of objective occurrence based on the historical data as depicted inFIG. 5 d. For instance, the objective occurrence data solicitationmodule 103 of the computing device 10 soliciting (e.g., via networkinterface 120 or via user interface 122) data indicating occurrence of aparticular or a particular type of objective occurrence (e.g.,requesting performance of shares of particular stock) based on thehistorical data 72 (e.g., historical data 72 that indicates that theuser 20* is happy when the shares of particular stocks rise).

In some implementations, the solicitation operation 304 of FIG. 3 mayinclude an operation 550 for soliciting data indicating one or moreattributes associated with occurrence of the at least one objectiveoccurrence as depicted in FIG. 5 d. For instance, the objectiveoccurrence data solicitation module 103 of the computing device 10soliciting (e.g., via network interface 120 or via user interface 122)data indicating one or more attributes associated with occurrence of theat least one objective occurrence (e.g., how hard or how long did itrain on Tuesday?).

In some implementations, the solicitation operation 304 may include anoperation 551 for soliciting the data indicating occurrence of at leastone objective occurrence by requesting access to the data indicatingoccurrence of the at least one objective occurrence as depicted in FIG.5 d. For instance, the objective occurrence data solicitation module 103of the computing device 10 soliciting (e.g., via network interface 120)the data indicating occurrence of at least one objective occurrence byrequesting access to the data indicating occurrence of the at least oneobjective occurrence (e.g., by requesting access to the file containingthe data or to the location of the data or to the data itself).

In various embodiments, the objective occurrence data acquisitionoperation 306 of FIG. 3 may include one or more additional operations asillustrated in FIGS. 6 a to 6 c. For example, in some implementations,the objective occurrence data acquisition operation 306 may include anoperation 602 for receiving the objective occurrence data via a userinterface as depicted in FIG. 6 a. For instance, the objectiveoccurrence data user interface reception module 226 (see FIG. 2 c) ofthe computing device 10 receiving the objective occurrence data 70* viaa user interface 122 (e.g., a key pad, a touchscreen, an audio systemincluding a microphone, an image capturing system such as a digital orvideo camera, or other user interfaces 122).

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 604 for receiving the objectiveoccurrence data from at least one of a wireless network or a wirednetwork as depicted in FIG. 6 a. For instance, the objective occurrencedata network interface reception module 227 of the computing device 10receiving (e.g., via the network interface 120) the objective occurrencedata 70* from at least one of a wireless and/or a wired network 40.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 606 for receiving the objectiveoccurrence data via one or more blog entries as depicted in FIG. 6 a.For instance, the reception module 224 of the computing device 10receiving (e.g., via network interface 120) the objective occurrencedata 70 a or 70 c via one or more blog entries (e.g., microblogentries).

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 608 for receiving the objectiveoccurrence data via one or more status reports as depicted in FIG. 6 a.For instance, the reception module 224 of the computing device 10receiving (e.g., via network interface 120) the objective occurrencedata 70* via one or more status reports (e.g., as generated by the user20* or by one or more third parties 50).

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 610 for receiving the objectiveoccurrence data from the user as depicted in FIG. 6 a. For instance, thereception module 224 of the computing device 10 receiving (e.g., vianetwork interface 120 or via the user interface 122) the objectiveoccurrence data 70* from the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 612 for receiving the objectiveoccurrence data from one or more third party sources as depicted in FIG.6 a. For instance, the reception module 224 of the computing device 10receiving (e.g., via network interface 120) the objective occurrencedata 70* from one or more third party sources (e.g., other users 20*,healthcare entities, content providers, or other third party sources).

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 614 for receiving the objectiveoccurrence data from one or more sensors configured to sense one or moreobjective occurrences as depicted in FIG. 6 a. For instance, thereception module 224 of the computing device 10 receiving (e.g., vianetwork interface 120) the objective occurrence data 70* from one ormore sensors 35 (e.g., a physiological sensing device, a physicalactivity sensing device such as a pedometer, a GPS, and so forth)configured to sense one or more objective occurrences.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 616 for acquiring at least onetime stamp associated with occurrence of at least one objectiveoccurrence as depicted in FIG. 6 b. For instance, the time stampacquisition module 230 (see FIG. 2 c) of the computing device 10acquiring (e.g., via the network interface 120, via the user interface122 as provided by the user 20*, or by automatically generating) atleast one time stamp associated with occurrence of at least oneobjective occurrence.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 618 for acquiring an indicationof at least one time interval associated with occurrence of at least oneobjective occurrence as depicted in FIG. 6 b. For instance, the timeinterval acquisition module 231 of the computing device 10 acquiring(e.g., via the network interface 120, via the user interface 122 asprovided by the user 20*, or by automatically generating) an indicationof at least one time interval associated with occurrence of at least oneobjective occurrence.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 619 for acquiring an indicationof at least a temporal relationship between the at least one objectiveoccurrence and occurrence of the at least one subjective user state asdepicted in FIG. 6 b. For instance, the temporal relationshipacquisition module 232 of the computing device 10 acquiring (e.g., viathe network interface 120, via the user interface 122 as provided by theuser 20*, or by automatically generating) an indication of at least atemporal relationship (e.g., before, after, or at least partiallyconcurrently) between the at least one objective occurrence andoccurrence of the at least one subjective user state.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 620 for acquiring data indicatingat least one objective occurrence and one or more attributes associatedwith the at least one objective occurrence as depicted in FIG. 6 b. Forinstance, the objective occurrence data acquisition module 104 of thecomputing device 10 acquiring data indicating at least one objectiveoccurrence (e.g., ingestion of a medicine or food item) and one or moreattributes (e.g., quality, quantity, brand, and/or source of themedicine or food item ingested) associated with the at least oneobjective occurrence.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 622 for acquiring data indicatingat least one objective occurrence of an ingestion by the user of amedicine as depicted in FIG. 6 b. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of an ingestion by the user20* of a medicine (e.g., a dosage of a beta blocker).

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 624 for acquiring data indicatingat least one objective occurrence of an ingestion by the user of a fooditem as depicted in FIG. 6 b. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of an ingestion by the user20* of a food item (e.g., an orange).

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 626 for acquiring data indicatingat least one objective occurrence of an ingestion by the user of anutraceutical as depicted in FIG. 6 b. For instance, the objectiveoccurrence data acquisition module 104 of the computing device 10acquiring (e.g., via the network interface 120 or via the user interface122) data indicating at least one objective occurrence of an ingestionby the user 20* of a nutraceutical (e.g. broccoli).

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 628 for acquiring data indicatingat least one objective occurrence of an exercise routine executed by theuser as depicted in FIG. 6 b. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of an exercise routine(e.g., working out on a exercise machine such as a treadmill) executedby the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 630 for acquiring data indicatingat least one objective occurrence of a social activity executed by theuser as depicted in FIG. 6 c. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of a social activity (e.g.,hiking with friends) executed by the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 632 for acquiring data indicatingat least one objective occurrence of an activity performed by a thirdparty as depicted in FIG. 6 c. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of an activity (e.g., bosson a vacation) performed by a third party 50.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 634 for acquiring data indicatingat least one objective occurrence of a physical characteristic of theuser as depicted in FIG. 6 c. For instance, the objective occurrencedata acquisition module 104 of the computing device 10 acquiring (e.g.,via the network interface 120 or via the user interface 122) dataindicating at least one objective occurrence of a physicalcharacteristic (e.g., a blood sugar level) of the user 20*. Note that aphysical characteristic such as a blood sugar level could be determinedusing a device such as a glucometer and then reported by the user 20*,by a third party 50, or by the device (e.g., glucometer) itself.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 636 for acquiring data indicatingat least one objective occurrence of a resting, a learning or arecreational activity by the user as depicted in FIG. 6 c. For instance,the objective occurrence data acquisition module 104 of the computingdevice 10 acquiring (e.g., via the network interface 120 or via the userinterface 122) data indicating at least one objective occurrence of aresting (e.g., sleeping), a learning (e.g., reading), or a recreationalactivity (e.g., a round of golf) by the user 20*.

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 638 for acquiring data indicatingat least one objective occurrence of an external event as depicted inFIG. 6 c. For instance, the objective occurrence data acquisition module104 of the computing device 10 acquiring (e.g., via the networkinterface 120 or via the user interface 122) data indicating at leastone objective occurrence of an external event (e.g., rain storm).

In some implementations, the objective occurrence data acquisitionoperation 306 may include an operation 640 for acquiring data indicatingat least one objective occurrence related to a location of the user asdepicted in FIG. 6 c. For instance, the objective occurrence dataacquisition module 104 of the computing device 10 acquiring (e.g., viathe network interface 120 or via the user interface 122) data indicatingat least one objective occurrence related to a location (e.g., workoffice at a first point or interval in time) of the user 20*. In someinstances, such data may be provided by the user 20* via the userinterface 122 (e.g., in the case where the computing device 10 is alocal device) or via the mobile device 30 (e.g., in the case where thecomputing device 10 is a network server). Alternatively, such data maybe provided directly by a sensor device 35 such as a GPS device, or by athird party 50.

Referring back to FIG. 3, the correlation operation 308 may include oneor more additional operations in various alternative implementations.For example, in various implementations, the correlation operation 308may include an operation 702 for correlating the subjective user statedata with the objective occurrence data based, at least in part, on adetermination of at least one sequential pattern associated with the atleast one subjective user state and the at least one objectiveoccurrence as depicted in FIG. 7 a. For instance, the correlation module106 of the computing device 10 correlating the subjective user statedata 60 with the objective occurrence data 70* based, at least in part,on a determination (e.g., as made by the sequential patterndetermination module 236) of at least one sequential pattern associatedwith the at least one subjective user state and the at least oneobjective occurrence.

In various alternative implementations, operation 702 may include one ormore additional operations. For example, in some implementations,operation 702 may include an operation 704 for correlating thesubjective user state data with the objective occurrence data based, atleast in part, on a determination of whether the at least one subjectiveuser state occurred within a predefined time increment from incidence ofthe at least one objective occurrence as depicted in FIG. 7 a. Forinstance, the correlation module 106 of the computing device 10correlating the subjective user state data 60 with the objectiveoccurrence data 70* based, at least in part, on a determination by the“within predefined time increment determination” module 238 (see FIG. 2d) of whether the at least one subjective user state occurred within apredefined time increment from incidence of the at least one objectiveoccurrence.

In some implementations, operation 702 may include an operation 706 forcorrelating the subjective user state data with the objective occurrencedata based, at least in part, on a determination of whether the at leastone subjective user state occurred before, after, or at least partiallyconcurrently with incidence of the at least one objective occurrence asdepicted in FIG. 7 a. For instance, the correlation module 106 of thecomputing device 10 correlating the subjective user state data 60 withthe objective occurrence data 70* based, at least in part, on adetermination by the temporal relationship determination module 239 ofwhether the at least one subjective user state occurred before, after,or at least partially concurrently with incidence of the at least oneobjective occurrence.

In some implementations, operation 702 may include an operation 708 forcorrelating the subjective user state data with the objective occurrencedata based, at least in part, on referencing of historical data asdepicted in FIG. 7 a. For instance, the correlation module 106 of thecomputing device 10 correlating the subjective user state data 60 withthe objective occurrence data 70* based, at least in part, onreferencing by the historical data referencing module 241 of historicaldata 72 (e.g., population trends such as the superior efficacy ofibuprofen as opposed to acetaminophen in reducing toothaches in thegeneral population, user medical data such as genetic, metabolome, orproteome information, historical sequential patterns particular to theuser 20* or to the overall population such as people having a hangoverafter drinking excessively, and so forth).

In various implementations, operation 708 may include one or moreadditional operations. For example, in some implementations, operation708 may include an operation 710 for correlating the subjective userstate data with the objective occurrence data based, at least in part,on the historical data indicative of a link between a subjective userstate type and an objective occurrence type as depicted in FIG. 7 a. Forinstance, the correlation module 106 of the computing device 10correlating the subjective user state data 60 with the objectiveoccurrence data 70* based, at least in part, on the historical datareferencing module 241 referencing historical data 72 indicative of alink between a subjective user state type and an objective occurrencetype (e.g., historical data 72 suggests or indicate a link between aperson's mental well-being and exercise).

In some instances, operation 710 may further include an operation 712for correlating the subjective user state data with the objectiveoccurrence data based, at least in part, on a historical sequentialpattern as depicted in FIG. 7 a. For instance, the correlation module106 of the computing device 10 correlating the subjective user statedata 60 with the objective occurrence data 70* based, at least in part,on a historical sequential pattern (e.g., a historical sequentialpattern that indicates that people feel more alert after exercising).

In some implementations, operation 708 may include an operation 714 forcorrelating the subjective user state data with the objective occurrencedata based, at least in part, on historical medical data associated withthe user as depicted in FIG. 7 a. For instance, the correlation module106 of the computing device 10 correlating the subjective user statedata 60 with the objective occurrence data 70* based, at least in part,on historical medical data associated with the user 20*(e.g., genetic,metabolome, or proteome information or medical records of the user 20*or of others related to, for example, diabetes or heart disease).

In some implementations, operation 702 may include an operation 716 forcomparing the at least one sequential pattern to a second sequentialpattern to determine whether the at least one sequential pattern atleast substantially matches with the second sequential pattern asdepicted in FIG. 7 b. For instance, the sequential pattern comparisonmodule 242 of the computing device 10 comparing the at least onesequential pattern to a second sequential pattern to determine whetherthe at least one sequential pattern at least substantially matches withthe second sequential pattern.

In various implementations, operation 716 may further include anoperation 718 for comparing the at least one sequential pattern to asecond sequential pattern related to at least a second subjective userstate associated with the user and a second objective occurrence todetermine whether the at least one sequential pattern at leastsubstantially matches with the second sequential pattern as depicted inFIG. 7 b. For instance, the sequential pattern comparison module 242 ofthe computing device 10 comparing the at least one sequential pattern toa second sequential pattern related to at least a second subjective userstate associated with the user 20* and a second objective occurrence todetermine whether the at least one sequential pattern at leastsubstantially matches with the second sequential pattern. In otherwords, comparing the at least one subjective user state and the at leastone objective occurrence associated with the one sequential pattern tothe at least a second subjective user state and the at least a secondobjective occurrence associated with the second sequential pattern inorder to determine whether they substantially match (or do not match) aswell as to determine whether the temporal or time relationshipsassociated with the one sequential pattern and the second sequentialpattern substantially match.

In some implementations, the correlation operation 308 of FIG. 3 mayinclude an operation 720 for correlating the subjective user state datawith the objective occurrence data at a server as depicted in FIG. 7 b.For instance, the correlation module 106 of the computing device 10correlating the subjective user state data 60 with the objectiveoccurrence data 70* when the computing device 10 is a network server.

In some implementations, the correlation operation 308 may include anoperation 722 for correlating the subjective user state data with theobjective occurrence data at a handheld device as depicted in FIG. 7 b.For instance, the correlation module 106 of the computing device 10correlating the subjective user state data 60 with the objectiveoccurrence data 70* when the computing device 10 is a handheld device.

In some implementations, the correlation operation 308 may include anoperation 724 for correlating the subjective user state data with theobjective occurrence data at a peer-to-peer network component device asdepicted in FIG. 7 b. For instance, the correlation module 106 of thecomputing device 10 correlating the subjective user state data 60 withthe objective occurrence data 70* when the computing device 10 is apeer-to-peer network component device.

Referring to FIG. 8 illustrating another operational flow 800 inaccordance with various embodiments. Operational flow 800 includesoperations that mirror the operations included in the operational flow300 of FIG. 3. These operations include a subjective user state dataacquisition operation 802, an objective occurrence data solicitationoperation 804, an objective occurrence data acquisition operation 806,and a correlation operation 808 that correspond to and mirror thesubjective user state data acquisition operation 302, the objectiveoccurrence data solicitation operation 304, the objective occurrencedata acquisition operation 306, and the correlation operation 308,respectively, of FIG. 3.

In addition, operational flow 800 includes a presentation operation 810for presenting one or more results of the correlating as depicted inFIG. 8. For example, the presentation module 108 of the computing device10 presenting (e.g., transmitting via a network interface 120 orproviding via the user interface 122) one or more results of thecorrelating operation as performed by the correlation module 106.

In various embodiments, the presentation operation 810 may include oneor more additional operations as depicted in FIG. 9. For example, insome implementations, the presentation operation 810 may include anoperation 902 for indicating the one or more results of the correlatingvia a user interface. For instance, the user interface indication module254 (see FIG. 2 e) of the computing device 10 indicating (e.g.,displaying or audibly indicating) the one or more results (e.g., in theform of an advisory, a warning, an alert, a prediction, and so forth ofa future or past result) of the correlating operation performed by thecorrelation module 106 via a user interface 122 (e.g., display monitor,touchscreen, or audio system including one or more speakers).

In some implementations, the presentation operation 810 may include anoperation 904 for transmitting the one or more results of thecorrelating via a network interface. For instance, the network interfacetransmission module 252 (see FIG. 2 e) of the computing device 10transmitting the one or more results (e.g., in the form of an advisory,a warning, an alert, a prediction, and so forth of a future or pastresult) of the correlating operation performed by the correlation module106 via a network interface 120 (e.g., NIC).

In some implementations, the presentation operation 810 may include anoperation 906 for presenting an indication of a sequential relationshipbetween the at least one subjective user state and the at least oneobjective occurrence. For instance, the sequential relationshippresentation module 256 of the computing device 10 presenting (e.g.,transmitting via the network interface 120 or indicating via userinterface 122) an indication of a sequential relationship between the atleast one subjective user state (e.g., headache) and the at least oneobjective occurrence (e.g., drinking beer).

In some implementations, the presentation operation 810 may include anoperation 908 for presenting a prediction of a future subjective userstate associated with the user resulting from a future objectiveoccurrence. For instance, the prediction presentation module 258 of thecomputing device 10 a prediction of a future subjective user stateassociated with the user 20* resulting from a future objectiveoccurrence. An example prediction might state that “if the user drinksfive shots of whiskey tonight, the user will have a hangover tomorrow.”

In some implementations, the presentation operation 810 may include anoperation 910 for presenting a prediction of a future subjective userstate associated with the user resulting from a past objectiveoccurrence. For instance, the prediction presentation module 258 of thecomputing device 10 presenting a prediction of a future subjective userstate associated with the user 20* resulting from a past objectiveoccurrence. An example prediction might state that “the user will have ahangover tomorrow since the user drank five shots of whiskey tonight.”

In some implementations, the presentation operation 810 may include anoperation 912 for presenting a past subjective user state associatedwith the user in connection with a past objective occurrence. Forinstance, the past presentation module 260 of the computing device 10presenting a past subjective user state associated with the user 20* inconnection with a past objective occurrence. An example of such apresentation might state that “the user got depressed the last time itrained.”

In some implementations, the presentation operation 810 may include anoperation 914 for presenting a recommendation for a future action. Forinstance, the recommendation module 262 of the computing device 10presenting a recommendation for a future action. An examplerecommendation might state that “the user should not drink five shots ofwhiskey.”

Operation 914 may, in some instances, include an additional operation916 for presenting a justification for the recommendation. For instance,the justification module 264 of the computing device 10 presenting ajustification for the recommendation. An example justification mightstate that “the user should not drink five shots of whiskey because thelast time the user drank five shots of whiskey, the user got ahangover.”

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware and software implementations of aspects of systems; theuse of hardware or software is generally (but not always, in that incertain contexts the choice between hardware and software can becomesignificant) a design choice representing cost vs. efficiency tradeoffs.Those having skill in the art will appreciate that there are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; alternatively, if flexibility is paramount, theimplementer may opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware. Hence, there are several possible vehicles bywhich the processes and/or devices and/or other technologies describedherein may be effected, none of which is inherently superior to theother in that any vehicle to be utilized is a choice dependent upon thecontext in which the vehicle will be deployed and the specific concerns(e.g., speed, flexibility, or predictability) of the implementer, any ofwhich may vary. Those skilled in the art will recognize that opticalaspects of implementations will typically employ optically-orientedhardware, software, and or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orany combination thereof can be viewed as being composed of various typesof “electrical circuitry.” Consequently, as used herein “electricalcircuitry” includes, but is not limited to, electrical circuitry havingat least one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of randomaccess memory), and/or electrical circuitry forming a communicationsdevice (e.g., a modem, communications switch, or optical-electricalequipment). Those having skill in the art will recognize that thesubject matter described herein may be implemented in an analog ordigital fashion or some combination thereof.

Those having skill in the art will recognize that it is common withinthe art to describe devices and/or processes in the fashion set forthherein, and thereafter use engineering practices to integrate suchdescribed devices and/or processes into data processing systems. Thatis, at least a portion of the devices and/or processes described hereincan be integrated into a data processing system via a reasonable amountof experimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.Furthermore, it is to be understood that the invention is defined by theappended claims.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should typically be interpreted to mean at least the recitednumber (e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.).

In those instances where a convention analogous to “at least one of A,B, or C, etc.” is used, in general such a construction is intended inthe sense one having skill in the art would understand the convention(e.g., “a system having at least one of A, B, or C” would include butnot be limited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). It will be further understood by those within the artthat virtually any disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” will be understood to include the possibilities of “A”or “B” or “A and B.”

1.-103. (canceled)
 104. A computationally-implemented system,comprising: means for acquiring subjective user state data includingdata indicating at least one subjective user state associated with auser; means for soliciting, in response to the acquisition of thesubjective user state data, objective occurrence data including dataindicating occurrence of at least one objective occurrence; means foracquiring the objective occurrence data; and means for correlating thesubjective user state data with the objective occurrence data.
 105. Thecomputationally-implemented system of claim 104, wherein said means foracquiring subjective user state data including data indicating at leastone subjective user state associated with a user comprises: means forreceiving the subjective user state data.
 106. Thecomputationally-implemented system of claim 105, wherein said means forreceiving the subjective user state data comprises: means for receivingthe subjective user state data via a user interface.
 107. Thecomputationally-implemented system of claim 105, wherein said means forreceiving the subjective user state data comprises: means for receivingthe subjective user state data via a network interface.
 108. Thecomputationally-implemented system of claim 107, wherein said means forreceiving the subjective user state data via a network interfacecomprises: means for receiving data indicating the at least onesubjective user state via an electronic message generated by the user.109. The computationally-implemented system of claim 107, wherein saidmeans for receiving the subjective user state data via a networkinterface comprises: means for receiving data indicating the at leastone subjective user state via a blog entry generated by the user. 110.The computationally-implemented system of claim 107, wherein said meansfor receiving the subjective user state data via a network interfacecomprises: means for receiving data indicating the at least onesubjective user state via a status report generated by the user.111-120. (canceled)
 121. The computationally-implemented system of claim104, wherein said means for acquiring subjective user state dataincluding data indicating at least one subjective user state associatedwith a user comprises: means for acquiring data indicating at least onesubjective mental state of the user.
 122. (canceled)
 123. Thecomputationally-implemented system of claim 104, wherein said means foracquiring subjective user state data including data indicating at leastone subjective user state associated with a user comprises: means foracquiring data indicating at least one subjective physical state of theuser.
 124. (canceled)
 125. The computationally-implemented system ofclaim 104, wherein said means for acquiring subjective user state dataincluding data indicating at least one subjective user state associatedwith a user comprises: means for acquiring data indicating at least onesubjective overall state of the user.
 126. (canceled)
 127. Thecomputationally-implemented system of claim 104, wherein said means foracquiring subjective user state data including data indicating at leastone subjective user state associated with a user comprises: means foracquiring a time stamp associated with occurrence of the at least onesubjective user state.
 128. The computationally-implemented system ofclaim 104, wherein said means for acquiring subjective user state dataincluding data indicating at least one subjective user state associatedwith a user comprises: means for acquiring an indication of a timeinterval associated with occurrence of the at least one subjective userstate.
 129. The computationally-implemented system of claim 104, whereinsaid means for acquiring subjective user state data including dataindicating at least one subjective user state associated with a usercomprises: means for acquiring an indication of a temporal relationshipbetween occurrence of the at least one subjective user state andoccurrence of the at least one objective occurrence. 130-133. (canceled)134. The computationally-implemented system of claim 104, wherein saidmeans for soliciting, in response to the acquisition of the subjectiveuser state data, objective occurrence data including data indicatingoccurrence of at least one objective occurrence comprises: means forsoliciting from the user the data indicating occurrence of at least oneobjective occurrence.
 135. The computationally-implemented system ofclaim 134, wherein said means for soliciting from the user the dataindicating occurrence of at least one objective occurrence comprises:means for soliciting the data indicating an occurrence of at least oneobjective occurrence via user interface. 136-137. (canceled)
 138. Thecomputationally-implemented system of claim 134, wherein said means forsoliciting from the user the data indicating occurrence of at least oneobjective occurrence comprises: means for soliciting the data indicatingan occurrence of at least one objective occurrence via a networkinterface.
 139. The computationally-implemented system of claim 134,wherein said means for soliciting from the user the data indicatingoccurrence of at least one objective occurrence comprises: means forrequesting the user to confirm occurrence of at least one objectiveoccurrence.
 140. The computationally-implemented system of claim 134,wherein said means for soliciting from the user the data indicatingoccurrence of at least one objective occurrence comprises: means forrequesting the user to select at least one objective occurrence from aplurality of indicated alternative objective occurrences. 141.(canceled)
 142. The computationally-implemented system of claim 134,wherein said means for soliciting from the user the data indicatingoccurrence of at least one objective occurrence comprises: means forrequesting the user to provide an indication of occurrence of at leastone objective occurrence with respect to occurrence of the at least onesubjective user state.
 143. The computationally-implemented system ofclaim 134, wherein said means for soliciting from the user the dataindicating occurrence of at least one objective occurrence comprises:means for requesting the user to provide an indication of occurrence ofat least one objective occurrence associated with a particular type ofobjective occurrences.
 144. The computationally-implemented system ofclaim 134, wherein said means for soliciting from the user the dataindicating occurrence of at least one objective occurrence comprises:means for requesting the user to provide an indication of a time ortemporal element associated with occurrence of the at least oneobjective occurrence.
 145. The computationally-implemented system ofclaim 144, wherein said means for requesting the user to provide anindication of a time or temporal element associated with occurrence ofthe at least one objective occurrence comprises: means for requestingthe user to provide an indication of a point in time associated with theoccurrence of the at least one objective occurrence.
 146. Thecomputationally-implemented system of claim 144, wherein said means forrequesting the user to provide an indication of a time or temporalelement associated with occurrence of the at least one objectiveoccurrence comprises: means for requesting the user to provide anindication of a time interval associated with the occurrence of the atleast one objective occurrence.
 147. The computationally-implementedsystem of claim 134, wherein said means for soliciting from the user thedata indicating occurrence of at least one objective occurrencecomprises: means for requesting the user to provide an indication oftemporal relationship between occurrence of the at least one objectiveoccurrence and occurrence of the at least one subjective user state.148. The computationally-implemented system of claim 104, wherein saidmeans for soliciting, in response to the acquisition of the subjectiveuser state data, objective occurrence data including data indicatingoccurrence of at least one objective occurrence comprises: means forsoliciting from one or more third party sources the data indicatingoccurrence of at least one objective occurrence.
 149. Thecomputationally-implemented system of claim 148, wherein said means forsoliciting from one or more third party sources the data indicatingoccurrence of at least one objective occurrence comprises: means forrequesting from one or more other users the data indicating occurrenceof at least one objective occurrence.
 150. Thecomputationally-implemented system of claim 148, wherein said means forsoliciting from one or more third party sources the data indicatingoccurrence of at least one objective occurrence comprises: means forrequesting from one or more healthcare entities the data indicatingoccurrence of at least one objective occurrence.
 151. Thecomputationally-implemented system of claim 148, wherein said means forsoliciting from one or more third party sources the data indicatingoccurrence of at least one objective occurrence comprises: means forrequesting from one or more content providers the data indicatingoccurrence of at least one objective occurrence.
 152. Thecomputationally-implemented system of claim 148, wherein said means forsoliciting from one or more third party sources the data indicatingoccurrence of at least one objective occurrence comprises: means forrequesting from one or more third party sources the data indicatingoccurrence of at least one objective occurrence that occurred at aspecified point in time.
 153. The computationally-implemented system ofclaim 148, wherein said means for soliciting from one or more thirdparty sources the data indicating occurrence of at least one objectiveoccurrence comprises: means for requesting from one or more third partysources the data indicating occurrence of at least one objectiveoccurrence that occurred during a specified time interval.
 154. Thecomputationally-implemented system of claim 104, wherein said means forsoliciting, in response to the acquisition of the subjective user statedata, objective occurrence data including data indicating occurrence ofat least one objective occurrence comprises: means for soliciting fromone or more sensors the data indicating occurrence of at least oneobjective occurrence.
 155. The computationally-implemented system ofclaim 154, wherein said means for soliciting from one or more sensorsthe data indicating occurrence of at least one objective occurrencecomprises: means for configuring the one or more sensors to collect andprovide the data indicating occurrence of at least one objectiveoccurrence.
 156. The computationally-implemented system of claim 154,wherein said means for soliciting from one or more sensors the dataindicating occurrence of at least one objective occurrence comprises:means for directing or instructing the one or more sensors to collectand provide the data indicating occurrence of at least one objectiveoccurrence.
 157. The computationally-implemented system of claim 104,wherein said means for soliciting, in response to the acquisition of thesubjective user state data, objective occurrence data including dataindicating occurrence of at least one objective occurrence comprises:means for soliciting the data indicating occurrence of at least oneobjective occurrence in response to the acquisition of the subjectiveuser state data and based on historical data.
 158. Thecomputationally-implemented system of claim 157, wherein said means forsoliciting the data indicating occurrence of at least one objectiveoccurrence in response to the acquisition of the subjective user statedata and based on historical data comprises: means for soliciting thedata indicating occurrence of at least one objective occurrence based,at least in part, on one or more historical sequential patterns. 159.The computationally-implemented system of claim 157, wherein said meansfor soliciting the data indicating occurrence of at least one objectiveoccurrence in response to the acquisition of the subjective user statedata and based on historical data comprises: means for soliciting thedata indicating occurrence of at least one objective occurrence based,at least in part, on medical data of the user.
 160. Thecomputationally-implemented system of claim 157, wherein said means forsoliciting the data indicating occurrence of at least one objectiveoccurrence in response to the acquisition of the subjective user statedata and based on historical data comprises: means for soliciting thedata indicating occurrence of at least one objective occurrence based,at least in part, on historical data indicative of a link between asubjective user state type and an objective occurrence type.
 161. Thecomputationally-implemented system of claim 157, wherein said means forsoliciting the data indicating occurrence of at least one objectiveoccurrence in response to the acquisition of the subjective user statedata and based on historical data comprises: means for soliciting thedata indicating occurrence of at least one objective occurrence, thesoliciting prompted, at least in part, by the historical data.
 162. Thecomputationally-implemented system of claim 157, wherein said means forsoliciting the data indicating occurrence of at least one objectiveoccurrence in response to the acquisition of the subjective user statedata and based on historical data comprises: means for soliciting dataindicating occurrence of a particular or a particular type of objectiveoccurrence based on the historical data.
 163. (canceled)
 164. Thecomputationally-implemented system of claim 104, wherein said means forsoliciting, in response to the acquisition of the subjective user statedata, objective occurrence data including data indicating occurrence ofat least one objective occurrence comprises: means for soliciting thedata indicating occurrence of at least one objective occurrence byrequesting access to the data indicating occurrence of the at least oneobjective occurrence. 165-208. (canceled)